Systems for translating sentences between languages using language-independent semantic structures and ratings of syntactic constructions

ABSTRACT

A method and computer system for translating sentences between languages from an intermediate language-independent semantic representation is provided. On the basis of comprehensive understanding about languages and semantics, exhaustive linguistic descriptions are used to analyze sentences, to build syntactic structures and language independent semantic structures and representations, and to synthesize one or more sentences in a natural or artificial language. A computer system is also provided to analyze and synthesize various linguistic structures and to perform translation of a wide spectrum of various sentence types. As result, a generalized data structure, such as a semantic structure, is generated from a sentence of an input language and can be transformed into a natural sentence expressing its meaning correctly in an output language. The method and computer system can be applied to in automated abstracting, machine translation, natural language processing, control systems, Internet information retrieval, etc.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in part of U.S. patent applicationSer. No. 11/548,214, filed Oct. 10, 2006 now U.S. Pat. No. 8,078,450.This application also claims benefit of U.S. provisional patentapplication Ser. No. 60/888,057, filed Feb. 2, 2007. This application isalso related to U.S. patent application Ser. No. 11/690,099, filedconcurrently and U.S. patent application Ser. No. 11/690,102, filedconcurrently. Each of the aforementioned related patent applications isherein incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the invention generally relate to the field of automatedtranslation of natural-language sentences using linguistic descriptionsand various applications in such areas as automated abstracting, machinetranslation, natural language processing, control systems, informationsearch (including on the Internet), semantic Web, computer-aidedlearning, expert systems, speech recognition/synthesis and others.

2. Description of the Related Art

The ability to understand, speak, and write one or more languages is anintegral part of human development to interact and communicate within asociety. Various language analysis/synthesis approaches have been usedto dissect a given language, analyze its linguistic structure in orderto understand the meanings of a word, a sentence in the given language,extract information from the word, the sentence, and, if necessary,translate into another language or synthesize into another sentence,which expresses the same semantic meaning in some natural or artificiallanguage.

Prior machine translation (MT) systems differ in the approaches andmethods that they use and also in their abilities to recognize variouscomplex language constructs and produce quality translation of textsfrom one language into another. According to their core principles,these systems can be divided into the following groups.

One of the traditional approaches is based on translation rules ortransformation rules and is called Rule-Based MT (RBMT). This approach,however, is rather limited when it comes to working with complexlanguage phenomena. In the recent years no significant breakthroughshave been achieved within this field. The best known systems of thistype are SYSTRAN and PROMPT. The known RBMT systems, however, usuallypossess restricted syntactic models and simplified dictionarydescriptions where language ambiguities are artificially removed.

Rule-based concept has evolved into Model-Based MT (MBMT) which is basedon linguistic models. Implementing a MBMT system to produce qualitytranslation demands considerable effort to create linguistic models andcorresponding descriptions for specific languages. Evolution of MBMTsystems is connected with developing complex language models on alllevels of language descriptions. The need in today's modern worldrequires translation between many different languages. Creating suchMBMT systems is only possible within a large-scale project to integratethe results of engineering and linguistic research. There is a need forsuch integration since it was never been completed before.

Another traditional approach is Knowledge-Based MT (KBMT) which usessemantic descriptions. While the MBMT approach is based on knowledgeabout a language, the KBMT approach considers translation as a processof understanding based on real knowledge about the World. Presently,interest in Knowledge-Based Machine Translation (KBMT) has been waning.

Example-Based MT (EBMT) relates to machine translation systems usingautomated analysis of examples, which is very similar toStatistics-Based MT (SBMT). In recent years, the SBMT approach hasreceived a strong impetus from the following factors: appearance ofTranslation Memory (TM) systems and availability of powerful andrelatively affordable bilingual electronic resources, such as TMdatabases created by corporations and translation agencies, electroniclibraries, and specialized Internet corpora. The TM systems havedemonstrated their practical efficiency when translating recurrent textfragments on the basis of minimal knowledge about languages such thatresearchers and developers are encouraged to try and create advanced andrelatively exhaustive SBMT systems.

Most machine translation systems, both rule-based and statistics-based,concentrate on proper transfer of language information directly betweena source sentence and an output sentence and usually do not require anyfull-fledged intermediary data structures to explicate the meaning ofthe sentence being translated. For example, a system based on linguisticmodels would know how to build thousands of syntactic variants of verbphrases-constituents. A system which is based on purely statisticalapproach would not know anything about the connections between thesevariants and would not be able to obtain a correct translation of onephrase on the basis of another. In addition, most-used probabilistic(statistic) approaches and statistics-based systems have a commondrawback of taking no consideration of semantics. As a result, there isno guarantee that the translated (or generated) sentence has the samemeaning as the original sentence.

Thus, even though some linguistic approaches have been proposed, most ofthem have not resulted in any useful algorithms or industrialapplications because of poor performance in translating completesentences. Complex sentences, which may express different shades ofmeaning, or the author's attitude and/or have different styles or genre,or which may be very long and contain various punctuation marks andother special symbols, have not been successfully generated/translatedby prior art systems, language generation programs, or machinetranslation systems. It is especially difficult to translate or generatecomplex sentences, such as those found in technical texts,documentation, internet articles, journals, and the like and is yet tobe done.

Accordingly, there exists a need for a method and system for translatingnatural language sentences between languages.

SUMMARY OF THE INVENTION

The present invention generally relates to methods, computer-readablemedia, devices and systems for translating a sentence into an outputlanguage. In one embodiment, a method of translating a sentence from onesource language into another output language includes analyzing thesource sentence using information from linguistic descriptions of thesource language, constructing a language-independent semantic structureto represent the meaning of the source sentence, and generating anoutput sentence from the language-independent semantic structure torepresent the meaning of the source sentence in the output languageusing information from linguistic descriptions of the output language.

In another embodiment, a method of translating the meaning of a sentencefrom an input language into an output language includes analyzing themeaning of the sentence using information from linguistic descriptionsof the source language, performing a rough syntactic analysis on thesentence to generate a graph of generalized constituents, and performinga precise syntactic analysis on the graph of the generalizedconstituents to generate one or more syntactic trees to represent thesentence from the graph of the generalized constituents. Alanguage-independent semantic structure is constructed from the one ormore syntactic trees to represent the meaning of the sentence and anoutput sentence is synthesized from the language-independent semanticstructure to represent the meaning of the sentence in the outputlanguage using information from linguistic descriptions of the outputlanguage.

In another embodiment, a method of representing the meaning of a sourcesentence in a source language into an output language includes analyzingthe meaning of the source sentence using information from linguisticdescriptions of the source language, constructing a language-independentsemantic structure to represent the meaning of the source sentence, andbuilding a syntactic structure in the output language from thelanguage-independent semantic structure using syntactic descriptions andmorphological descriptions of the output language. An output sentence torepresent the meaning of the source sentence is synthesized from thesyntactic structure in the output language.

In another embodiment, a method is provided to represent the meaning ofa source sentence from a source language and includes providing alanguage-independent semantic structure to represent the meaning of thesource sentence, synthesizing a syntactic structure from thelanguage-independent semantic structure using information which includeslexical descriptions, semantic descriptions, syntactic descriptions, andmorphological descriptions of the output language, and constructing anoutput sentence to represent the meaning of the source sentence in anoutput language.

In another embodiment, a method of generating a sentence in an outputlanguage is provided. The method includes performing a lexical selectionon a language-independent semantic structure of the sentence usinglexical descriptions and semantic descriptions in the output language,building a syntactic structure from the language-independent semanticstructure using syntactic descriptions and morphological descriptions ofthe output language, performing a morphological synthesis on thesyntactic structure using morphological descriptions of the outputlanguage, and constructing the sentence in the output language. Themethod further includes determining a linear order and restoringmovements on the syntactic structure of the sentence.

In another embodiment, a computer readable medium comprisinginstructions for causing a computing system to carry out steps fortranslating a source sentence from a source language into an outputlanguage includes analyzing the meaning of the source sentence usinginformation from linguistic descriptions of the source language, andconstructing a language-independent semantic structure to represent themeaning of the source sentence. The steps also include generating anoutput sentence from the language-independent semantic structure torepresent the meanings of the source sentence in the output languageusing information from linguistic descriptions of the output language.

In another embodiment, a computer readable medium having instructionsfor causing a computing system to a language synthesizing method isprovided. The computer readable medium includes instructions for thecomputer system to perform steps including obtaining alanguage-independent semantic structure for the sentence, performing alexical selection on the language-independent semantic structure of thesentence using lexical descriptions and semantic descriptions, buildinga surface structure from the language-independent semantic structureusing syntactic descriptions and lexical descriptions of the outputlanguage, determining a linear order and restoring movements on thesurface structure of the sentence to be synthesized, performing amorphological synthesis on the surface structure using morphologicaldescriptions of the output language, and generating the sentence in theoutput language.

In still another embodiment, a computer system adapted to translate themeanings of a source sentence from an input language into an outputlanguage is provided. The computer system includes a source sentenceanalyzer adapted to analyze the meanings of the source sentence usinginformation from linguistic descriptions of the source language and toconstruct a language-independent semantic structure to represent themeanings of the source sentence, and an output sentence synthesizeradapted to synthesize an output sentence to represent the meanings ofthe source sentence in an output language from the language-independentsemantic structure using information from linguistic descriptions of theoutput language.

In still another embodiment, a computer system adapted to synthesize asentence into an output language, includes a semantic synthesizeradapted to perform a semantic analysis on the a language-independentsemantic structure for the sentence, a lexical synthesizer adapted toperform a lexical selection on the language-independent semanticstructure of the sentence using lexical descriptions and semanticdescriptions in the output language, a surface structure builder adaptedto build a surface structure from the language-independent semanticstructure using syntactic descriptions and morphological descriptions ofthe output language, a surface structure analyzer adapted to determine alinear order and restoring movements on the surface structure of thesentence to be synthesized, and a morphological synthesizer adapted toperform a morphological synthesis on the surface structure usingmorphological descriptions of the output language to construct thesentence in the output language.

In still another embodiment, a computer system adapted to represent asource sentence from a source language into an output sentence in anoutput language includes a lexical-morphological analyzer adapted toperform a lexical analysis and a lexical-morphological analysis on eachelement of the source sentence to generate a lexical-morphologicalstructure of the source sentence, a syntactic analyzer adapted toperform a syntactic analysis on the lexical-morphological structure ofthe source sentence, and a semantic analyzer adapted to perform asemantic analysis on the source sentence and generate alanguage-independent semantic structure for the source sentence. Thecomputer system also includes a lexical synthesizer adapted to perform alexical selection on the language-independent semantic structure of thesource sentence using lexical descriptions and semantic descriptions inthe output language, and a surface structure builder adapted to build asurface structure from the language-independent semantic structure usingsyntactic descriptions and lexical descriptions of the output languageand construct the output sentence in the output language. The computersystem further includes a morphological synthesizer adapted to perform amorphological synthesis on the surface structure using morphologicaldescriptions of the output language and synthesize the output sentence.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates one embodiment of a method for translating a sourcesentence in a source language into an output sentence in an outputlanguage.

FIG. 2 illustrates another embodiment of a method for translating asource sentence in a source language into an output sentence in anoutput language.

FIG. 3 illustrates still another embodiment of a method for translatinga source sentence in a source language into an output sentence in anoutput language.

FIG. 4 illustrates still another embodiment of a method for transforminga language-independent semantic structure into the output sentence 114in an output language.

FIG. 5A illustrates a flow diagram of a method according to oneembodiment of the invention.

FIG. 5B illustrates converting of the source sentence 112 into theoutput sentence 114 through various structures according to an exemplaryembodiment of the invention.

FIG. 6 is a diagram illustrating language descriptions according to oneexemplary embodiment of the invention.

FIG. 7 is a diagram illustrating morphological descriptions according toone exemplary embodiment of the invention.

FIG. 8 is a diagram illustrating syntactic descriptions according to oneexemplary embodiment of the invention.

FIG. 9 is a diagram illustrating semantic descriptions according to oneexemplary embodiment of the invention.

FIG. 10 is a diagram illustrating lexical descriptions according to oneexemplary embodiment of the invention.

FIG. 11 is a lexical-morphological structure for an exemplary sentence“This boy is smart, he'll succeed in life.” according to one embodimentof the invention.

FIG. 12 is a process flow diagram illustrating one example of a roughsyntactic analysis according to one embodiment of the invention.

FIG. 13 is an exemplary graph of generalized constituents for thesentence “This boy is smart, he'll succeed in life.” according to oneembodiment of the invention.

FIG. 14 is a process flow diagram illustrating one example of a precisesyntactic analysis according to one embodiment of the invention.

FIG. 15 is an exemplary schematic representation of a syntactic treeaccording to one embodiment of the invention.

FIG. 16 is an exemplary syntactic tree of the above mentioned sentence“This boy is smart, he'll succeed in life.”

FIG. 17 is a best syntactic tree for the exemplary sentence extractedfrom the graph of generalized constituents from FIG. 13.

FIG. 18 is an exemplary best syntactic structure for the exemplarysentence with non-tree links generated on the basis of a syntactic treeshown on FIG. 17.

FIG. 19 is one example of the best syntactic structure with semanticparents of lexical meanings and their grammemes, non-tree linksgenerated and deep slots for the exemplary sentence.

FIG. 20 is a process flow diagram illustrating one example of a semanticanalysis according to one embodiment of the invention.

FIG. 21 is an exemplary semantic structure with semantemes and exemplaryanalysis rules according to one or more embodiments of the invention.

FIG. 22 is a process flow diagram illustrating an output naturallanguage sentence synthesis according to one or more embodiments of theinvention.

FIG. 23 is a diagram exemplifying various components of a syntacticstructure of synthesized sentence according to one or more embodimentsof the invention.

FIG. 24 is an exemplary surface (syntactic) structure for a synthesizedRussian sentence which correspond to English sentence “This boy issmart, he'll succeed in life.” according to one embodiment of theinvention.

FIG. 25 is a block diagram of one illustrative embodiment of a computersystem where a method of translating natural sentences can beimplemented.

FIG. 26 is another block diagram illustrating a computer system inaccordance with one embodiment of the invention.

FIG. 27 is a block diagram of a machine translation system in accordancewith one embodiment of the invention.

FIG. 28 is another block diagram of a machine translation system and itsinteraction with other applications in accordance with one embodiment ofthe invention.

DETAILED DESCRIPTION

Embodiments of the invention provide methods, computer-readable media,and computer systems configured to efficiently and completely translatea source sentence in an input language into an output language usinglanguage-independent, universal semantic concepts and structures. Thesurface syntactical structures and language-independent semanticstructures as described herein are very useful for translating sentencesbetween languages. Exhaustive linguistic descriptions are used toanalyze a sentence and generate language-independent semantic structuresfor a source sentence. Problems of syntactical and semantic ambiguitieswhich may appear during the process of transition and translation can bereliably handled.

The language-independent semantic structures are generated for thesource sentence in an input language and are transformed into surfacesyntactic structures in an output language to generate an outputsentence in the output language. The input and output languages can beany natural or artificial languages for which all necessary linguisticdescriptions can be created.

In one embodiment, syntactical and semantic descriptions are joined intocommon semantic structures using linguistic descriptions to translateand generate the output sentence. These linguistic descriptions mayinclude morphological descriptions, lexical descriptions, syntacticdescriptions, which are language-specific, as well as semanticdescriptions which are language-independent. Semantic descriptions areused to describe language-independent semantic features of variouslanguages to express a meaning of any sentence in language-independentsemantic terms.

In another embodiment, language-independent semantic structures areefficiently transitioned to surface syntactical structures between theinput and output languages, which can be the same or differentlanguages. All possible syntactic structures without any restrictions onthe syntax of the source sentence and output sentence can be generated.Output sentences that can be generated in the output language include awide spectrum of sentences from simple sentences to very complex ones.Linguistic knowledge is taken into consideration to make sure that allthe information and meanings represented by a semantic structure of asource sentence are truly and faithfully expressed from the sourcesentence in an input language into an output sentence in an outputlanguage.

The syntactic structures generated for a source sentence and/or anoutput sentence are limited only by the syntax of an input language oroutput language without any other artificial language restrictions.Maximal use of linguistic knowledge and natural language descriptions isemployed to generate a language-independent semantic structure whichcontains a large amount of various data information about the meaning ofthe source sentence. The natural language descriptions may besufficiently comprehensive to cover all of language phenomena manifestedin written discourse. In one aspect, the linguistic descriptions andcomputer systems disclosed herein may be employed to exhaustively andcomprehensively generate the most probable, most suitable andsyntactically correct surface structures through a language-independentsemantic structure for sentences between input and output languages.

Embodiments of the invention include methods and computer readablystorage media to execute the methods for constructing a linguisticknowledge based model of a natural language to create all requiredlanguage descriptions; for generating a language-independent semanticstructure and/or representation to express the meaning of the sourcesentences in an input language; for transforming the semantic structureinto output sentences in an output language. Embodiments of theinvention also provide computer readable media, language translationcomputer systems, instructions and means for carrying out methods of theinvention. An environment for monitoring the sentence analysis andgeneration process is also provided. Embodiments of the invention may beprovided to be implemented in various forms, formats, and also adaptedto be stored on a computer-readable medium, executed as a computerprogram, or as part of a device for generating a sentence of a givenlanguage from a language-independent semantic structure.

FIG. 1 illustrates a flow chart of a method 100 for translating a sourcesentence 112 in a source language into an output sentence 114 in anoutput language. At step 110, the source sentence is analyzed usinginformation from linguistic descriptions of the source language. Thelinguistic descriptions useful for analyzing the source sentence mayinclude morphological descriptions, syntactic descriptions, lexicaldescriptions, and semantic descriptions of various languages. In somecases, all available linguistic models and knowledge about naturallanguages may be arranged in database and configured to analysis asource sentence and synthesize an output sentence when all availableexhaustive linguistic descriptions are used. Integral models fordescribing the syntax and semantics of the source language are used inorder to recognize the meanings of the source sentence, analyze andtranslate complex language structures, and correctly convey informationencoded in the source sentence.

The invention is superior to the known art as it uses various linguisticdescriptions of a given natural language to reflect in reality all thecomplexities of the natural language, rather than simplified orartificial descriptions, without the danger of a combinatorialexplosion. A principle of integral and purpose-driven recognition, e.g.,hypotheses about the structure of a part of a sentence are verifiedwithin the hypotheses about the structure of the whole sentence, isimplemented during the analysis stage as well as the synthesis stage.This principle avoids the need of analyzing numerous parsing anomalousvariants. All available information from linguistic descriptions of theoutput language is used.

At step 120, after the source sentence is analyzed, alanguage-independent semantic structure is constructed to represent themeaning of the source sentence. The language-independent semanticstructure is a generalized data structure in language-independentform/format as an intermediate language-independent semanticrepresentation when translating the source sentence from the sourcelanguage into the output language. Such a novel language-independentsemantic structure generated for each source sentence to be analyzed canbe used to accurately describe the meaning of the source sentence andcan be easily applied to various applications, including, but notlimited to automated abstracting, machine translation, control systems,internet information retrieval, etc. As a result, the maximum accuracyin conveying the meanings of the source sentence during translation fromone language into the same or another language is achieved.

At step 130, after the language-independent semantic structure isconstructed, the output sentence is generated from thelanguage-independent semantic structure in order to represent themeaning of the source sentence in the output language. Accordingly, ahigh level of accuracy in translating an input natural-language sentenceinto the same or another natural-language is achieved.

FIG. 2 illustrates another example of a method 200 for translating thesource sentence 112 in a source language into the output sentence 114 inan output language. At step 210, the source sentence is analyzed usinginformation from linguistic descriptions of the source language.

When analyzing the meaning of the source sentence, a two-step analysisalgorithm (e.g., rough syntactic analysis and precise syntacticanalysis) is implemented to make use of linguistic models and knowledgeat various levels to calculate probability ratings and to generate themost probable syntactic structure, e.g., a best syntactic structure.Accordingly, at step 220, a rough syntactic analysis is performed on thesource sentence to generate a graph of generalized constituents forfurther syntactic analysis.

At step 230, a precise syntactic analysis is performed on the graph ofgeneralized constituents to generate one or more syntactic trees torepresent the source sentence from the graph of the generalizedconstituents. This novel two-step syntactic analysis approach ensuresthe meaning of the source sentence to be accurately represented into abest syntactic structure which is chosen from the one or more syntactictrees. In addition, hypotheses for a portion of a sentence for analyzingits meaning and generating an appropriate language structure usingavailable linguistic descriptions are verified within the hypothesesabout the language structure for the whole sentence. This approachavoids analyzing numerous parsing variants, which are known to beinvalid, such that one or more syntactic trees are rated, each syntactictree representing the whole sentence, in order to select the bestsyntactic structure.

At step 240, after the source sentence is analyzed, alanguage-independent semantic structure is constructed to represent themeaning of the source sentence. At step 250, after thelanguage-independent semantic structure is constructed, the outputsentence is generated from the language-independent semantic structureusing information from linguistic descriptions of the output language inorder to represent the meaning of the source sentence in the outputlanguage.

FIG. 3 illustrates another example of a method 300 for translating thesource sentence 112 in a source language into the output sentence 114 inan output language. At step 310, the source sentence is analyzed usinginformation from linguistic descriptions of the source language andafter the source sentence is analyzed, at step 320, alanguage-independent semantic structure is constructed to represent themeaning of the source sentence.

At step 330, a syntactic structure in an output language is built fromthe language-independent semantic structure using syntactic descriptionsand lexical descriptions of the output language. At step 340, the outputsentence is generated from the syntactic structure in the outputlanguage.

FIG. 4 illustrates another example of a method 400 for transforming alanguage-independent semantic structure 412 into the output sentence 114in an output language. At step 410, a lexical selection is performed onthe language-independent semantic structure of a sentence using semanticdescriptions and lexical descriptions of the output language. At step420, a syntactic structure of the sentence is built from thelanguage-independent semantic structure using syntactic descriptions andlexical descriptions of the output language.

At step 430, a linear order on the syntactic structure of the sentenceis determined and restoring movements is executed. At step 440, amorphological synthesis is performed on the syntactic structure usingmorphological descriptions of the output language before the outputsentence 114 is constructed in the output language at step 450.

FIG. 5A illustrates a method 500 for translating the source sentence 112from a source language into the output sentence 114 in an outputlanguage via a language-independent semantic structure according to anexemplary embodiment of the invention. FIG. 5B illustrates transformingof the source sentence 112 into the output sentence 114 through variousstructures according to an exemplary embodiment of the invention.

As shown in FIGS. 5A and 5B, the method 500 for translating a sourcesentence 112 in a source/input language into an output sentence 114includes using linguistic descriptions adapted to perform various stepsof analysis and synthesis. The linguistic descriptions may includemorphological descriptions 501, syntactic descriptions 502, lexicaldescriptions 503, and semantic descriptions 504.

At step 510, a lexical analysis is performed on the source sentence 112in a source/input language. At step 520, a lexical-morphologicalanalysis is also performed on the source sentence 112 to generate alexical-morphological structure 522 of the source sentence 112 usinginformation from the morphological descriptions 501 and the lexicaldescriptions 501 of the source language.

Then, a syntactic analysis is performed on the lexical-morphologicalstructure 522 of the source sentence 112. In one embodiment of theinvention, the syntactic analysis includes a rough syntactic analysisand a precise syntactic analysis.

At step 530, the rough syntactic analysis is performed on thelexical-morphological structure 522 of the source sentence 112 togenerate a graph of generalized constituents 532. Performing the roughsyntactic analysis may require the use of the syntactical descriptions502, the lexical descriptions 503 of the source language, and thesemantic descriptions 504.

At step 540, the precise analysis is performed on the graph ofgeneralized constituents 532. The precise analysis may include, but notlimited to, generating one or more syntactic trees, obtaining ratingscores for the one or more syntactic trees, generating non-tree links,and building a best syntactic structure 546. Performing the precisesyntactic analysis may require the use of the syntactical descriptions502, the lexical descriptions 503, and the semantic descriptions 504.Step 544 indicates that the syntactic analysis is performed repeatedlyif the best syntactic structure 546 is not successfully built.

At step 550, a semantic analysis is performed to transition the bestsyntactic structure 546 of the source sentence in the natural languageinto a language-independent semantic structure 552. Thelanguage-independent semantic structure 552 is generated to fully conveythe meaning of the source sentence in the source natural language andrepresent the source sentence in a language-independent form.

At step 560, syntactic structure synthesis is performed on thelanguage-independent semantic structure 552 to build a surface/syntacticstructure 562. The syntactic structure synthesis may include, but notlimited to, performing a lexical selection on the language-independentsemantic structure 552 using the linguistic descriptions of the outputlanguage. The syntactic structure synthesis may require the use of thesyntactical descriptions 502, the lexical descriptions 503, and thesemantic descriptions 504 of the output language.

At step 570, morphological synthesis is performed on thesurface/syntactic structure 562 to generate the output sentence 114. Themorphological synthesis may require the use of the morphologicaldescriptions 501 and the lexical descriptions 503 of the outputlanguage.

FIG. 6 illustrates language descriptions 610 including morphologicaldescriptions 501, lexical descriptions 503, syntactic descriptions 502,and semantic descriptions 504, and interrelationship between them. Amongthem, the morphological descriptions 501, the lexical descriptions 503,and the syntactic descriptions 502 are language-specific. Each of theselanguage descriptions 610 can be created for each source language, andtaken together; they represent a model of the source language. Thesemantic descriptions 504, however, are language-independent and areused to describe language-independent semantic features of objects,meanings, processes, events, etc. in various languages and to constructlanguage-independent semantic structures.

As shown in FIG. 6, the morphological descriptions 501, the lexicaldescriptions 503, the syntactic descriptions 502, and the semanticdescriptions 104 are related. More specifically, lexical descriptions504 and morphological descriptions 501 are related by a link 621,because a specified lexical meaning in the lexical description 503 mayhave a morphological model represented as one or more grammatical valuesfor the specified lexical meaning. For example, one or more grammaticalvalues can be represented by different sets of grammemes in agrammatical system of the morphological descriptions 501.

In addition, as shown by a link 622, a lexical meaning in the lexicaldescriptions 503 may also have one or more surface models correspondingto the syntactic descriptions 502 for the given lexical meaning. Asrepresented by a link 623, the lexical descriptions 503 can be connectedwith the semantic descriptions 504. Therefore, the lexical descriptions503 and the semantic descriptions 504 may be combined into“lexical-semantic descriptions”, such as a lexical-semantic dictionary1004.

As shown by a link 624, the syntactic descriptions 502 and the semanticdescriptions 504 are also related. For examples, diatheses of thesyntactic descriptions 502 can be considered as the “interface” betweenthe language-specific surface models and language-independent deepmodels of the semantic description 504. Examples of diatheses and deepmodels are further described in conjunction with FIGS. 8 and 9.

FIG. 7 illustrates exemplary morphological descriptions. As shown, thecomponents of the morphological descriptions 501 include, but are notlimited to, word-inflexion description 710, grammatical system 720(e.g., grammemes), and word-formation description 730. In oneembodiment, grammatical system 720 includes a set of grammaticalcategories, such as, “Part of speech”, “Case”, “Gender”, “Number”,“Person”, “Reflexivity”, “Tense”, “Aspect”, etc. and their meanings,hereafter referred to as “grammemes”. For example, part of speechgrammemes may include “Adjective”, “Noun”, “Verb”, etc.; case grammemesmay include “Nominative”, “Accusative”, “Genitive”, etc.; and gendergrammemes may include “Feminine”, “Masculine”, “Neuter”, etc.

Word-inflexion description 710 describes how the main form of a wordform may change according to its case, gender, number, tense, etc. andbroadly includes all possible forms for a given word. Word-formation 730describes which new words may be generated involving a given word. Thegrammemes are units of the grammatical systems 720 and, as shown by alink 722 and a link 724, the grammemes can be used to build theword-inflexion description 710 and the word-formation description 730.

According to one aspect of the invention, when establishing syntacticrelationships for elements of a source sentence, one or more constituentmodels are used. A constituent may include a contiguous group of wordsfrom the source sentence and behaves as one entity. Each constituent mayinclude a core word and can include child constituents at lower levels.A child constituent is a dependent constituent and may be attached toother constituents (as parent constituents) for building the syntacticstructure of a source sentence.

FIG. 8 illustrates exemplary syntactic descriptions. The components ofthe syntactic descriptions 502 may include, but are not limited to,surface models 810, surface slot descriptions 820, referential andstructural control description 830, government and agreement description840, non-tree syntax description 850, and analysis rules 860. Thesyntactic descriptions 502 are used to construct possible syntacticstructures of a source sentence from a given source language, takinginto account free linear word order, non-tree syntactic phenomena (e.g.,coordination, ellipsis, etc.), referential relationships, and otherconsiderations.

The surface models 810 are represented as aggregates of one or moresyntactic forms (“syntforms” 812) in order to describe possiblesyntactic structures of sentences in a given language. In general, anylexical meaning of a language is linked to their surface (i.e.,syntactic) models 810, which represent constituents which are possiblewhen the lexical meaning functions as a “core” and includes a set ofsurface slots of child elements, a description of the linear order,diatheses, among others.

While the surface models 810 may be represented by syntforms 812, eachsyntform 812 may include a certain lexical meaning which functions as a“core” and may further include a set of surface slots 815 of its childconstituents, a linear order description 816, diatheses 817, grammaticalvalues 814, government and agreement descriptions 840, communicativedescriptions 880, among others, in relationship to the core of theconstituent.

The surface slot descriptions 820 as a part of syntactic descriptions502 are used to describe the general properties of the surface slots 815that are used in the surface models 810 of various lexical meanings inthe source language. The surface slots 815 are used to express syntacticrelationships between the constituents of the sentence. Examples of thesurface slot 815 may include “Subject”, “Object_Direct”,“Object_Indirect”, “Relative Clause”, among others.

As part of a syntactic description, any constituent model uses aplurality of surface slots 815 of child constituents along with theirlinear order descriptions 816 to describe the grammatical values 814 ofpossible fillers of these surface slots 815. The diatheses 817 representcorrespondences between the surface slots 815 and deep slots 914 (asshown in FIG. 9). The diatheses 817 are represented by the link 624between syntactic descriptions 502 and semantic descriptions 504 (asshown in FIG. 6). The communicative descriptions 880 describecommunicative order of the words in a sentence.

Any syntactic form, syntform 812, is a set of surface slots 815 coupledwith the linear order descriptions 816. One or more possibleconstituents for a lexical meaning of a word form of a source sentencemay be represented by surface syntactic models, e.g., the surface models810. Each constituent is viewed as the realization of the constituentmodel by means of selecting a corresponding syntform 812. The selectedsyntactic forms, the syntforms 812, are sets of surface slots 815 with aspecified linear order. Further, each surface slot in a syntform canhave grammatical and semantic restrictions on their fillers.

Linear order description 816 is represented as linear order expressionswhich are built to express a sequence in which various surface slots 815can occur in the sentence. The linear order expressions may includenames of variables, names of surface slots, parenthesis, grammemes,ratings, and the “or” or “and” operators, etc. For example, a linearorder description for the sentence, “Boys play football.” may berepresented as “Subject Core Object_Direct”, where “Subject,Object_Direct” are names of surface slots 815 corresponding to the wordorder. Fillers of the surface slots 815 are present by symbols in thesame order as in the linear order expression.

Different surface slots 815 may be in a strict and/or variablerelationship in the syntform 812. For example, parenthesis may be usedto build the linear order expressions and describe strict linear orderrelationships between different surface slots 815. SurfaceSlot1SurfaceSlot2 or (SurfaceSlot1 SurfaceSlot2) means that both surfaceslots are located in the same linear order expression, but only oneorder of these surface slots relative to each other is possible; namely,SurfaceSlot2 follows after SurfaceSlot1.

As another example, square brackets may be used to describe variablelinear order relationships between different surface slots 815 of thesyntform 812 in the linear order expressions. As such, [SurfaceSlot1SurfaceSlot2] indicates that any linear order of surface slots denotedby the variables SurfaceSlot1 and SurfaceSlot2 is possible.

The linear order expressions of the linear order description 816 maycontain grammatical values 814, expressed by grammemes, to which childconstituents correspond. In addition, two linear order expressions canbe joined by the operator |(<<OR>>). For example: (Subject CoreObject)|[Subject Core Object].

Communicative descriptions 880 describe a word order in the syntform 812from the point of view of communicative acts to be represented ascommunicative order expressions, which are similar to linear orderexpressions. The government and agreement description 840 contains rulesand restrictions on grammatical values of attached constituents whichare used during syntactic analysis.

Non-tree syntax descriptions 850 are related to processing variouslinguistic phenomena, such as, ellipsis and coordination, and are usedin syntactic structures transformations which are generated duringvarious steps of analysis according to embodiments of the invention. Thenon-tree syntax description 850 include ellipsis description 852,coordination description 854, as well as, referential and structuralcontrol description 856, among others.

Analysis rules 860, as a part of the syntactic descriptions 502, mayinclude, but are not limited to, semantemes calculating rules 862 andnormalization rules 864. Although analysis rules 860 are used during thestep of semantic analysis 550, the analysis rules 860 generally describeproperties of a specific language and are related to the syntacticdescriptions 502. The normalization rules 864 are generally used astransformational rules to describe transformations of semanticstructures which may be different in various languages.

FIG. 9 illustrates exemplary semantic descriptions. As stated above inreference to FIG. 5, semantic descriptions provide language-independentdescriptions of deep constituents, deep slots, and semantemes, etc. Thecomponents of the semantic descriptions 504 are alsolanguage-independent and may include, but are not limited to, a semantichierarchy 910, deep slots descriptions 920, a system of semantemes 930,and pragmatic descriptions 940.

Semantic hierarchy 910 may include semantic notions or semantic entitiesnamed semantic classes, arranged into hierarchical parent-childrelationships. In general, a child semantic class inherits mostproperties of its direct parent and all ancestral semantic classes. Forexample, semantic class SUBSTANCE is a child of semantic class ENTITYand at the same time it is a parent of semantic classes GAS, LIQUID,METAL, WOOD_MATERIAL, etc.

Each semantic class in the semantic hierarchy 910 is supplied with adeep model 912. The deep model 912 of the semantic class is a set of thedeep slots 914, which reflect the semantic roles of child constituentsin various sentences with objects of the semantic class as the core of aparent constituent and the possible semantic classes as fillers of deepslots. The deep slots 914 express semantic relationships betweenconstituents, including, for example, “agent”, “addressee”,“instrument”, “quantity”, etc. A child semantic class inherits andadjusts the deep model 912 of its direct parent semantic class

Deep slots descriptions 920 are used to describe the properties of thedeep slots 914 and reflect the semantic roles of child constituents inthe deep models 912. The deep slots descriptions 920 also containgrammatical and semantic restrictions on what could be the possiblefillers of the deep slots 914. The properties and restrictions for thedeep slots 914 and their possible fillers are very similar andoftentimes identical among different languages. Thus, the deep slots 914are language-independent.

System of semantemes 930 represents a set of semantic categories andsemantemes, which represent the meanings of the semantic categories. Asan example, a semantic category “DegreeOfComparison” can be used todescribe the degrees of comparison expressed by various forms ofadjectives, for example, “easy”, “easier” and “easiest”. Accordingly,the semantic category “DegreeOfComparison” may include such semantemesas, for example, “Positive”, “ComparativeHigherDegree”,“SuperlativeHighestDegree”, among others. As another example, a semanticcategory “RelationToReferencePoint” can be used to describe an order asbefore or after a reference point relative to some event or object,etc., and its semantemes may include, “Previous”, “Subsequent”, and theorder may be spatial or temporal in a broad sense. As yet anotherexample, “EvaluationObjective”, as a semantic category, may describe anobjective assessment, such as “Bad”, “Good”, etc.

The systems of semantemes 930 include language-independent semanticattributes which express semantic characteristics as well as stylistic,pragmatic and communicative characteristics. Semantemes can also be usedto express an atomic meaning which finds a regular grammatical and/orlexical expression in a language. By purpose and usage, the system ofsemantemes 930 may be divided into various kinds, including, but notlimited to, grammatical semantemes 932, lexical semantemes 934, andclassifying grammatical (differentiating) semantemes 936.

Grammatical semantemes 932 are used to describe grammatical propertiesof constituents when transforming a syntactic tree (a language dependentobject) into a semantic structure (a language independent object).Grammatical semantemes 932 can also be used to describe grammaticalproperties of constituents when synthesizing backwardly from a semanticstructure during syntactic structure synthesis 560 to a syntacticstructure. Lexical semantemes 934 describe specific properties ofobjects (for example, “being flat” or “being liquid”) and are used inthe deep slot descriptions 920 as restriction for deep slot fillers (forexample, for the verbs “face (with)” and “flood”, respectively).Classifying grammatical (differentiating) semantemes 936 expressdifferentiating properties of objects within a single semantic class.For example, in the semantic class, HAIRDRESSER, the semanteme<<RelatedToMen>> is assigned to the lexical meaning “barber”, unlikeother lexical meanings which also belong to this class, such as“hairdresser”, “hairstylist”, etc.

Pragmatic descriptions 940 are used to assign a corresponding theme,style or genre to texts and objects of the semantic hierarchy 910. Forexample, “Economic Policy”, “Foreign Policy”, “Justice”, “Legislation”,“Trade”, “Finance”, etc. Pragmatic properties can also be expressed bysemantemes. For example, pragmatic context may be considered whengenerating natural language sentences.

FIG. 10 illustrates exemplary lexical descriptions. The lexicaldescriptions 503 represent a plurality of lexical meanings 1012 in aspecific language. For each lexical meaning 1012, a link 1002 to itslanguage-independent semantic parent may be established to indicate thelocation of a given lexical meaning in the semantic hierarchy 910.

Each lexical meaning 1012 is connected with its deep model 912, which isdescribed in language-independent terms, and surface model 810, which islanguage-specific. Diatheses can be used as the interface between thesurface models 810 and the deep models 912 for each lexical meaning 1012or for a portion of a speech with some specific grammatical value. Oneor more diatheses 817 can be assigned to each surface slot 815 in eachsyntform 812 of the surface models 810.

While the surface model 810 describes the syntactic roles of surfaceslots and their fillers, the deep model 912 generally describes thesemantic roles of the surface slots and the fillers. A deep slotdescription 920 expresses the semantic types of possible fillers and/orthe properties or attributes of the objects denoted by the words of anynatural language.

Deep slot descriptions 920 are language-independent since differentlanguages may use the same deep slot to describe similar semanticrelationships or to express similar aspects of a real-life situation.Typically, the fillers of the deep slots 914 have the same semanticproperties even in different languages. Lexical meanings 1012 of alexical description of a language inherit properties of semantic classfrom its parent and adjust its deep model 912.

In addition, lexical meanings 1012 may contain their own characteristicsas well as inherit other characteristics from language-independentparent semantic class. These characteristics of the lexical meanings1012 may include grammatical values 1008 and semantic value 1010, whichcan be expressed as grammemes and semantemes, respectively.

Each surface model 810 of a lexical meaning includes one or moresyntforms 812. Every syntform, 812 of a surface model 810 may includeone or more surface slots 815 with their linear order description 816,one or more grammatical values 814 expressed as a set of grammaticalcharacteristics (grammemes), one or more semantic restrictions onsurface slot fillers, and one or more of the diatheses 817. Semanticrestrictions on a surface slot filler are a set of semantic classes,whose objects can fill a given surface slot. The diatheses 817 are partof relationship 624 between syntactic descriptions 502 and semanticdescriptions 504, and represent correspondences between the surfaceslots 815 and the deep slots 914 of the deep model 912.

Lexical Analysis and Lexical Morphological Analysis

Referring back to FIG. 5, the lexical analysis 510 is performed on thesource sentence 112 as represented in a source/input language, which maybe any natural language. In one embodiment, all the necessary languagedescriptions for the source sentence 112 are created. The sourcesentence 112 may be divided into a number of lexemes, elements, orunits, including all the words, word forms, gaps, spaces, andpunctuators, etc. present in the source sentence for building a lexicalstructure of the sentence. A lexeme is a meaningful linguistic unit thatis an item in the vocabulary, such as the lexical-semantic dictionary1004 of a language.

As shown in FIG. 5, the lexical-morphological analysis 520 is performedon the source sentence 112. During the lexical-morphological analysis520 each element of the source sentence 112 are searched in order tofind one or more word forms, which is not a space or a punctuator,assign one or more pairs of “lexical meaning-grammatical value”corresponding to each word form, and generate a lexical-morphologicalstructure for the source sentence 112. The lexical-morphologicalstructure of a sentence contains a complete set of pairs of “lexicalmeaning-grammatical value” for each unit of the lexical structure whichis not a space or a punctuator.

The morphological descriptions 501 for the source language (e.g., theword-inflexion description 710 and the word-formation description 730,etc.) are used to provide a set of lexemes for each word form. Eachlexeme may correspond to one or more (usually multiple) word forms, oneor more corresponding lexical meanings 1012 and grammatical values 1008obtained from the lexical description 503, an their correspondinggrammatical values 1008 obtained from the morphological descriptions501. The grammatical values 1008 are represented as a set of values ofgrammatical attributes (expressed in grammemes) of a word form. Examplesof these grammatical attributes include, but are not limited to, thepart of speech, number, gender, case, etc. A complete set of pairs of“lexical meaning-grammatical value” is generated for each word form inthe source sentence 112 and used to build the lexical-morphologicstructure for the source sentence 112.

FIG. 11 illustrates an example of the lexical-morphological structurefor the sentence “This boy is smart, he'll succeed in life.” accordingto one embodiment of the invention. The lexical-morphological structurehas a complete set of pairs of “lexical meaning-grammatical value” foreach unit of this sentence. For example, “II” may mean “shall” 1122 and“will” 1124 as its lexical meanings 1012. For the lexical meaning of“shall” 1122, the grammatical values 1008 is <Verb, GTVerbModal,ZeroType, Present, Nonnegative, Composite_II>, as shown in FIG. 11. Asanother example, the grammatical value 1008 for the lexical meaning“will” 1124 is <Verb, GTVerbModal, ZeroType, Present, Nonnegative,Irregular, Composite_II>.

During the initial stage of the lexical-morphological analysis 520,lemmatization (searching and assigning lexemes) and obtaining pairs oflexical meaning-grammatical value are concurrently made. The lexeme ofthe word form, its lemma, and morphological grammatical values for allelements for the source sentence 112 are searched and found by using thelexical descriptions 503. If there may be various grammatical values1008 for a word form found for a single category, thelexical-morphological structure may include all the possible grammaticalvalues 1008 connected by “or”.

For example, six pairs of “lexical meaning-grammatical value” are foundfor the word form “smart”, as shown in FIG. 11. As a result, the wordform “smart” may have the same lexical meaning of “smart” but six (6)different grammatical values 1008. Depending on its presence indifferent parts of speech, the word form “smart” may be Adjective, Verb,Adverb, Noun, etc, and there may be three different grammatical valuesfor Verb as the value of the “Part of speech” category, as shown in FIG.9. As another example, the word form “life” may have two lexicalmeaning-grammatical value pairs generated having the lexical meaning1132 of “life” paired with the grammatical value of <Adjective,DegreePositive, GTAdjectiveAttr> and lexical meaning 1134 of “life”paired with <Noun, Nominative|Accusative, GTNoun, Singular>.

In addition, different lexical meanings may correspond to the samelexeme, for example, the lexeme “smart” as an adjective has thefollowing lexical meanings with different semantics (as given, forexample, in the Oxford Thesaurus), including 1) “well dressed, wellturned out, fashionably dressed, etc.”; 2) “fashionable, stylish,high-class, exclusive, chic, fancy, etc.”; 3) “clever, bright,intelligent, sharp, sharp-witted, quick-witted, etc.”, among others.These different lexical meanings may have different language-independentsemantic parents, proper deep models, and proper surface models. Duringthe lexical analysis stage, all these lexical meanings are generalized,but the whole list of these lexical meanings is stored/saved in order touse their surface and deep models for further analysis.

Since every lexical meaning in any given language goes back to theparent semantic class and inherits some characteristics of the parentsemantic class, a corresponding lexical description 503 with its surfacemodel 810 and deep model 912 can be found in the lexical-semanticdictionary 1004. All the lexical descriptions 503 and the morphologicaldescriptions 501 related to all the lexical meanings 1012 of all wordforms of the source sentence 112 are used in the lexical-morphologicalanalysis 520. Once a complete set of pairs of “lexicalmeaning-grammatical value” for the source sentence 112 are made, mergingof the grammatical values 1008 are performed.

Once the lexical-morphological structure is constructed and generalizedgrammatical values, if generalization/merging is possible, are providedfor each word form, a syntactic analysis is performed. The syntacticanalysis may be performed in two steps, the rough syntactic analysis 530and the precise syntactic analysis 540, which are performed bottom-upand top-down, respectively.

Rough Syntactic Analysis

During the rough syntactic analysis, as shown on FIG. 12, a graph 1202of generalized constituents is build from the lexical-morphologicalstructure 1201 of the source sentence 112. All the possible surfacesyntactic models for each element of lexical-morphological structure1201 are applied, and all the possible constituents are built andgeneralized. A rough syntactic analyzer or its equivalents is adapted togenerate the graph 1202 of the generalized constituents from thelexical-morphological structure 1201 using the surface models 810, thedeep models 912, and the lexical-semantic dictionary 1004.

In one embodiment, all the possible syntactic descriptions and syntacticstructures for the source sentence 112 are considered and generalized.As a result, the graph 1202 of the generalized constituents is built,having each generalized constituent generalized from all the possibleconstituents for each element of the source sentence 112, and buildinggeneralized constituents are performed for all the elements of thesource sentence 112. The graph 1202 of generalized constituentsreflects, on a surface model level, all hypothetical possible syntacticrelationships between the words of the source sentence 112.

To build all possible constituents, every element of the source sentence112 which is not a space or a punctuator is viewed as a potential coreof a constituent. The building of the graph 1202 of generalizedconstituents starts with building those constituents which have only thecore word form and further expands to build constituents of the nextlevel by including neighboring constituents. For each pair of “lexicalmeaning-grammatical value” which corresponds to a non-trivial arc oflexical-morphological structure, its surface model 810 is initialized,attempting to attach other constituents in the surface slots 815 of thesyntforms 812 of its surface model 810 to the right and the leftneighboring constituents. If an appropriate syntform 812 is found in thesurface model 810 of the corresponding lexical meaning, the selectedlexical meaning may be the core of a new constituent.

The building of the graph 1202 of generalized constituents starts withbuilding those constituents which have only the core word form andfurther expands to build constituents of the next level by includingneighboring constituents. For each pair of “lexical meaning-grammaticalvalue” which corresponds to a non-trivial arc of lexical-morphologicalstructure, its surface model 810 is initialized, attempting to attachother constituents in the surface slots 815 of the syntforms 812 of itssurface model 810 to the right and the left neighboring constituents. Ifan appropriate syntform 812 is found in the surface model 810 of thecorresponding lexical meaning, the selected lexical meaning may be thecore of a new constituent.

The graph 1202 of generalized constituents is first built as a tree,from the leaves to the root (bottom up). Building of additionalconstituents is performed bottom-up by attaching child constituents toparent constituents via filling the surface slots 815 of parentconstituents to cover all the initial lexical units of the sourcesentence 112.

The root of the tree is the main clause, represented by a specialconstituent corresponding to various types of maximal units of a textanalysis (complete sentences, enumerations, titles, etc.). The core ofthe main clause is generally a predicate. During this process, the treeactually becomes a graph, because lower-level constituents (the leaves)can be included into different upper-level constituents (the root).

Some of the constituents which are built for the same element of thelexical-morphological structure may be generalized to obtain generalizedconstituents. Constituents are generalized by the lexical meanings 1012,by the grammatical values 814, for example, by parts of speech, by theirboundaries, among others. Constituents are generalized by theboundaries, since there may be very different syntactic relationships inthe sentence, and the same word may be included in differentconstituents. As a result of the rough syntactic analysis 530, the graph1202 of generalized constituents is built which represents the wholesentence.

FIG. 12 illustrates in further detail the rough syntactic analysis 530according to one or more embodiments of the invention. The roughsyntactic analysis 530 generally includes preliminary assembly 1210 ofthe constituents, building 1220 of generalized constituents, filtering1270, building 1230 of generalized constituent models, building 1240 agraph of generalized constituents, coordination processing 1250, andrestoring ellipsis 1260, among others.

The preliminary assembly 1210 of the constituents during the roughsyntactic analysis 530 is performed on the lexical-morphologicalstructure 1201 of the sentence to be analyzed, including certain wordgroups, the words in brackets, inverted commas, etc. Only one word inthe group (the constituent core) can attach or be attached toconstituents outside the group. The preliminary assembly 1210 isperformed early during the rough syntactic analysis 530 before building1220 of generalized constituents and building 1230 of the generalizedconstituent models to cover all the boundaries of the whole sentence.

Building 1220 of generalized constituents generally require that allpossible pairs of the lexical meaning 1012 and the grammatical value 814are found or assigned for each of the constituents and attach thesurface slots of the child constituents thereof to each of theconstituents. Lexical units of the source sentence 112 can form intocore constituents at bottom levels. Each constituent can be attached toa constituent at a higher level if the surface slots 815 of theconstituent at the higher level can be filled. Thus, the constituentsare further expanded to include the neighboring constituents built atprevious constituent building process until all of the possibleconstituents have been built to cover the entire sentence.

During rough syntactic analysis 530, the number of the differentconstituents which may be built and the syntactic relationships amongthem are considerably large, some of the surface models 810 of theconstituents are chosen to be filtered through the process of filtering1270 prior to and after the building the constituents in order togreatly reduce the number of the different constituents to beconsidered. Thus, at the early stage of the rough syntactic analysis530, the most suitable surface models and syntforms are selected on thebasis of a prior rating. Such prior rough ratings include ratings oflexical meanings, ratings of fillers, ratings of the correspondence tosemantic descriptions, among others.

The filtering 1270 during the rough syntactic analysis 530 includefiltering of a set of syntforms 812 performed prior to and during thebuilding 1220 of generalized constituents. The syntforms 812 and thesurface slots 815 are filtered a priori, and constituents are filteredafter they are built. The process of the filtering 1270 distills out anumber of syntforms including, but not limited to, those syntforms thatdo not correspond to the grammatical values of the constituent, thosesyntforms where none of the core slots can be filled, those syntformswith special slots which describe grammatical movement, among others. Aspecial slot, such as relativization and question, presupposing aspecial lexeme (relative or interrogative pronoun), is filtered out ifthe special lexeme is not present in the sentence.

In general, the syntax forms (syntforms 812) which do not have fillersfor at least one surface slot can be filtered and discarded. Inaddition, those lexical meanings 1012 which do not have syntforms 812with filled surface slots 815 are filtered and discarded. The roughsyntactic analysis 530 is impossible to succeed if there is no syntformand no filled surface slot, and as such the filtering 1270 is performed.

Once all possible constituents are built, the generalization procedureis performed for building 1220 of the generalized constituents. Allpossible homonyms and all possible meanings for elements of the sourcesentence which are capable of being present in the same part of a speechare condensed and generalized, and all possible constituents built inthis fashion are condensed into generalized constituents 1222.

A generalized constituent 1222 describes all the constituents with allthe possible boundaries in a given source sentence which have a wordform as the core constituent and various lexical meanings of this wordform. Since the constituents are generalized, a single constituent foreach lexical meaning corresponding to each entity of a sentence,including homonyms, is built, and their syntactic forms may be analyzedsimultaneously.

The building 1230 of generalized constituent models is performed and aset of models 1232 of generalized constituents having generalized modelsof all generalized lexemes are built. A generalized constituent model ofa lexeme contains a generalized deep model and a generalized surfacemodel. A generalized deep model of a lexeme includes the list of all ofthe deep slots which have the same lexical meaning for a lexeme,together with the descriptions of all the requirements for the fillersof the deep slots. A generalized surface model contains informationabout the syntforms 812, where the lexeme may occur, about the surfaceslots 815, about the diatheses correspondences 817 between surface slots815 and deep slots 914, and about the linear order description 816.

The syntforms 812 and the surface slots 815 that are significant forthis lexeme are selected with the help of the bit-mask. In addition,models of the generalized constituents are built because a constituentis generalized not only by lexical meanings and syntactic forms of itscore, but also by the fragments it fills. The use of the models of thegeneralized constituents reduces the number of wrong relationships andhelps to optimize the process to extract a syntactic tree so that allpossible boundaries are considered.

The generalized diatheses are built during the rough syntactic analysis530 as the correspondence between generalized surface models andgeneralized deep models. The list of all possible semantic classes forall the diatheses 817 of the lexeme is calculated for each surface slot815.

As shown in FIG. 12, information from the syntforms 812 of the syntacticdescriptions 502 as well as the semantic descriptions 504 are used tobuild the models 1232 of the generalized constituents. For example,dependent constituents are attached to each lexical meaning of asentence unit and the rough syntactic analysis 530 may also need todetermine whether a “candidate” constituent as a dependent constituentcan be a filler of the corresponding deep slot of the deep model 912 fora core constituent. Such compatibility analysis allows the wrongsyntactic relationships to be discarded early.

The building 1240 of the graph of the generalized constituents isperformed. The graph 1202 of generalized constituents which describesall possible syntactic structures of the entire sentence is built bylinking and assembling the generalized constituents 1222 to each other.The building 1240 of the graph of the generalized constituents isorganized via generating and processing of the queue of requests toattach one constituent to another constituent. In general, contact pairsof constituents representing contact groups of words in the sentence canbe included in the request queue.

A constituent can be attached to different surface slots of anotherconstituent and a child constituent can be attached to different parentconstituents. In each case, a request for attachment of one constituentto another constituent can be generated. The requests can be processedby a subsystem, such as a dispatcher 1290. If attachment to the selectedsurface slot is performed or found impossible, the request is removedfrom the queue of active request of the dispatcher 1290.

The dispatcher 1290 or any devices, systems, computer-readable media,adapted to perform the building 1240 of the graph of the generalizedconstituents can wait and search for new constituent pairs in order toput these constituent pairs into the dispatcher queue, such as bykeeping the right and left directions of the neighboring constituents ofa constituent. For example, during attaching a child constituent to theparent constituents, the left constituent pair of the child constituentis added to the left of the parent constituent and the right constituentpair of the child constituent is added to the right of the parentconstituent.

As shown in FIG. 12, the coordination processing 1250 is also performedon the graph 1202 of the generalized constituents. Coordination is alanguage phenomenon which is presented in sentences with enumerationand/or a coordinating conjunction, such as “and”, “or”, “but”, etc. Asimple example of a sentence with coordination—“John, Mary and Bill camehome.” In this case only one of coordinated child constituent isattached in the surface slot of a parent constituent during building1240 the graph of the generalized constituents. If a constituent, whichmay be a parent constituent, has a surface slot filled for a coordinatedconstituent, all coordinated constituents are taken and an attempt ismade to attach all these child constituents to the parent constituent,even if there is no contact or attachment between the coordinatedconstituents. During coordination processing 1250, the linear order andmultiple filling possibility of the surface slot are determined. If theattachment is possible, a proform which refers to the common childconstituent is created and attached. As shown in FIG. 12, thecoordination processor 1282 or other algorithms, devices, and computersubsystems can be adapted to perform the coordination processing 1250using coordination descriptions 852 in the building 1240 of the graph ofgeneralized constituents.

The building 1240 of the graph of the generalized constituents can beimpossible without ellipsis restoration 1260. Ellipsis is a languagephenomenon which is represented by the absence of core constituents.Ellipsis can also be related with coordination. The process of theellipsis restoration 1260 is also needed to restore a missingconstituent. An example of an elliptical English sentence is “Thepresident signed the agreement and the secretary [signed] the protocol.”As discussed above, the ellipsis restoration 1260 can be used togenerate the new request and new constituent pairs.

As shown in FIG. 12, the ellipsis processor 1280 or other algorithms,devices, and computer subsystems can be adapted to perform the ellipsisrestoration 1260. In addition, the ellipsis descriptions 852 whichcontain proform models can be adapted to aid the ellipsis processor 1280and process core ellipsis to build the graph 1202 of generalizedconstituents. Proforms may be auxiliary elements inserted into asentence when establishing non-tree links. A proform model may includetemplates (patterns) of syntforms. These proform templates determine therequired surface slots and their linear order. All constituents in thesentence for each proform are searched and the possibility to attach theconstituent to the first of the required slots of the syntform-templateis determined.

The coordination processing 1250 and the ellipsis restoration 1260 areperformed during each program cycle of the dispatcher 1290 after thebuilding 1240 of the graph of the generalized constituents and thebuilding 1240 may continue, as indicated by an arrow of returning back1242. If the ellipsis restoration 1260 is needed and called upon duringthe rough syntactic analysis 530 due to, for example, the presence ofconstituents left alone without any parent constituents being attachedto, only these constituents are processed.

The dispatcher 1290 stops when the active request queue is empty andcannot be refilled. The dispatcher 1290 can be a device, system, oralgorithm, which keeps all the information about the constituents thathave been modified. A constituent is considered modified if changes havebeen introduced to any of its properties which describe the sub-tree,including boundaries and the set of pre-child constituents. In addition,during the building 1240 of the generalized constituents 1222, clausesubstitution is performed. Clauses for direct speech and proper namesare substituted.

FIG. 13 is an example of a graph 1300 of generalized constituents forthe sentence “This boy is smart, he'll succeed in life.” Theconstituents are represented by rectangles, each constituent having alexeme as its core. Morphological paradigm (as a rule, the part ofspeech) of a constituent core is expressed by grammemes of the part ofspeech and displayed in broken brackets below the lexeme. Amorphological paradigm as a part of word-inflection description 710 ofmorphological description 501 contains all information aboutword-inflection of one or more part of speech. For example, since “life”can have two parts of speech: <Adjective> and <Noun> (which isrepresented by the generalized morphological paradigm <Noun&Pronoun>),two constituents for “life” are shown in the graph 1300.

Links in the graph 1300 represent filled surface slots of theconstituent cores. Slot names are displayed on the arrows of the graph.A constituent is formed by a lexeme-core which may have outgoing namedarrows which denotes surface slots 815 filled by child constituents. Anincoming arrow means attaching this constituent to a surface slot ofanother constituent. The graph 1300 is so complicated and has so manyarrows, because it shows all relationships which can be establishedbetween constituents of the sentence, “This boy is smart, he'll succeedin life.” Among them there are many relationships in the graph 1300,which, however, will be discarded. A value of said prior rough rating issaved by each arrow denoting a filled surface slot. Surface slot andrelationships with high rating scores are selected hierarchically duringsyntactic analysis.

Often several arrows may connect the same pairs of constituents. Itmeans that there are different acceptable surface models for this pairof constituents, and several surface slots of the parent constituent maybe independently filled by this child constituent. So, four surfaceslots named Object-Direct 1310, Object_Indirect_in 1320, Subject 1330and AdjunctTime 1335 of the parent constituent “succeed<Verb>” 1350 maybe independently filled by the child constituent “life<Noun&Pronoun>”1340 in accordance with surface model of the constituent“succeed<Verb>”. Thus, roughly speaking “in<Preposition>” 1345 and“life<Noun&Pronoun>” 1340 form a new constituent with the core “life”and it, in turn, form with “succeed<Verb>” 1350 four variants of anothernew constituents with the core “succeed<Verb>” which is attached toanother parent constituent, for example, to #NormalSentence<Clause> 1360in the surface slot Verb 1370, and to “boy<Noun&Pronoun>” 1390 in thesurface slot RelativClause_DirectFinite 1390 The marked element#NormalSentence<Clause>, being the “root”, corresponds to the wholesentence.

Precise Syntactic Analysis

FIG. 14 illustrates in detail the precise syntactic analysis 540performed to select the best syntactic structure 1402 according one ormore embodiments of the invention. The precise syntactic analysis 540 isperformed top-down from the higher levels to the bottom lower levels,from the node of the potential top of the graph 1202 of the generalizedconstituents down to its bottom-level child constituents.

The precise syntactic analysis 540 is performed to build a syntactictree, which is a tree of the best syntactic structure 1402, for thesource sentence. Many syntactic structures can be built and the mostprobable syntactic structure is obtained as the best syntactic structure1402. The best syntactic structure 1402 is obtained on the basis ofcalculating ratings using a priori ratings 1466 from the graph 1202 ofthe generalized constituents. The priori ratings 1466 include ratings ofthe lexical meanings, such as frequency (or probability), ratings ofeach of the syntactic constructions (e.g., idioms, collocations, etc.)for each element of the sentence, and the degree of correspondence ofthe selected syntactic constructions to the semantic descriptions of thedeep slots 914. Rating scores are calculated and obtained/stored.

Hypotheses about the overall syntactic structure of the sentence aregenerated. Each hypothesis is represented by a tree which is a subgraphof the graph 1202 of the generalized constituents to cover the entiresentence, and rating is calculated for each syntactic tree. During theprecise syntactic analysis 540, hypotheses about the syntactic structureof the source sentence are verified by calculating several types ofratings. These ratings are calculated as the degree of correspondence ofthe fillers of the surface slots 815 of the constituent to theirgrammatical and semantic descriptions, such as grammatical restrictions(e.g., the grammatical values 814) in the syntforms 812 and semanticrestrictions on the fillers of the deep slots 914 in the deep models912. Another types of ratings are the degree of correspondence of thelexical meanings 1012 to the pragmatic descriptions 940, which may beabsolute and/or relative probability ratings of the syntacticconstructions as denoted by the surface models 810, and the degree ofcompatibility of their lexical meanings, among others.

The calculated rating scores for each hypothesis may be obtained on thebasis of a priori rough ratings found during the rough syntacticanalysis 530. For example, a rough assessment is made for eachgeneralized constituent in the graph 1202 of the generalizedconstituents and ratings scores can be calculated. Various syntactictrees can be built with different ratings. Rating scores are obtained,and these calculated rating scores are used to generate hypotheses aboutthe overall syntactic structure of the sentence. To achieve this, thehypotheses with the highest rating are selected. These hypotheses aregenerated by advancing hypotheses about the structure of the childconstituents which are most probable in order to obtain the mostprobable hypothesis about the overall syntactic structure of thesentence. Ratings are performed during precise syntactic analysis untila satisfactory result is obtained and a best syntactic tree havinghighest rating can be built.

Those hypotheses with the most probable syntactic structure of a wholesentence can also be generated and obtained. From syntactic structure1402 variants with higher ratings to syntactic structure 1402 variantswith lower ratings, syntactic structure hypotheses are generated duringprecise syntactic analysis until a satisfactory result is obtained and abest syntactic tree which has the highest possible rating can be built.

The best syntactic tree is selected as the syntactic structurehypothesis with the highest rating value available from the graph 1202of the generalized constituents. This syntactic tree is considered asthe best (the most probable) hypothesis about the syntactic structure ofthe source sentence 112. Non-tree links in the tree are assigned, andaccordingly, the syntactic tree is transformed into a graph as the bestsyntactic structure 1402, representing the best hypothesis about thesyntactic structure of the source sentence 112. If non-treerelationships can not be assigned in the selected best syntactic tree,the syntactic tree with the second-best rating is selected as the bestsyntactic tree for further analysis.

When the precise syntactic analysis 540 is unsuccessful or the mostprobable hypotheses can not be found after initial precise syntacticanalysis, returning back 544 denoting unsuccessful syntactic structurebuilding from the precise syntactic analysis 540 back to the roughsyntactic analysis 530 is provided and all syntforms, not just the bestsyntforms, are considered during the syntactic analysis. If no bestsyntactic trees are found or the system has failed to define non-treerelationships in all the selected “best” trees, additional roughsyntactic analysis 530 may be performed taking into consideration “bad”syntform which were not analyzed before for the method of the invention.

As shown in FIG. 14, the precise syntactic analysis 540 may containvarious stages, including a preliminary stage, a stage 1450 forgenerating a graph of precise constituents, a stage 1460 for generatingsyntactic trees and differential selection of the best syntactic tree, astage 1470 for generating non-tree links and obtaining a best syntacticstructure, among others. The graph 1202 of generalized constituents isanalyzed during the preliminary stage which prepares the data for theprecise syntactic analysis 540.

The preliminary stage of the precise syntactic analysis 540 may includefragment specification 1410 and generating 1450 of a graph of preciseconstituents to obtain a graph of linear division 1440 and a graph ofprecise constituents 1430, respectively. A linear divisional graphbuilder 1415 and builder 1490 of precise constituents may be adapted toprocess the fragment specification 1410 for obtaining the graph oflinear division 1440 and the graph of precise constituents 1430. Inaddition, the models 1232 of the generalized constituents can be usedduring the building 1450 of the graph of precise constituents.

During the precise syntactic analysis 540, the precise constituents arebuilt recursively. Proper constituents are generated backwardly andrecursively. The precise constituents are built from the generalizedconstituents 1222 to initially perform the fragment specification 1410thereon. The building 1450 of the graph of precise constituents mayinclude reviewing the graph 1440 of linear division, recursivelybuilding the graph 1430 of the precise constituents which may containsfixed but not yet filled child slots, recursive performing the fragmentspecification 1410 for each graph arc lying on the way, and recursivefilling a child slot to attach a child precise constituent builtpreviously, among others. The generalized constituents 1222 are used tobuild the graph 1430 of precise constituents for generating one or moretrees of precise constituents. For each generalized constituent, itspossible boundaries and their child constituents are marked.

The stage 1460 for generating the syntactic trees is performed togenerate the best syntactic tree 1420. The stage 1470 for generatingnon-tree links may use the rules of establishing non-tree links and theinformation from syntactic structures 1475 of previous sentences toanalyze one or more best syntactic trees 1420 and select the bestsyntactic structure 1402 among the various syntactic structures. Agenerator 1485 for generating non-tree links is adapted to perform thestage 1470.

As shown in FIG. 14, the fragment specification 1410 of the precisesyntactic analysis 540 is performed initially to consider variousfragments which are continuous segments of a parent constituent. Eachgeneralized child constituent can be included into one or more parentconstituent in one or more fragments. The graph of linear division 1440(GLD) can be built as the result of the fragment specification 1410 toreflect the relationships of the parent constituent fragments with thecore and child constituents. Additionally, the surface slot for thecorresponding child constituents is assigned. The graph of lineardivision 1440 is the framework for building the graph 1430 of preciseconstituents. Precise constituents are nodes of the graph 1430 and oneor more trees of precise constituents are generated on the basis of thegraph 1430 of precise constituents.

The graph 1430 of precise constituents is an intermediate representationbetween the graph 1202 of generalized constituents and syntactic trees.Unlike a syntactic tree, the graph 1430 of precise constituents canstill have several alternative fillers for a surface slot. The preciseconstituents are formed into a graph such that a certain constituent canbe included into several alternative parent constituents in order tooptimize further analysis for selecting syntactic trees. Such anintermediate graph structure is rather compact for calculatingstructural ratings.

During the recursive stage 1450 for generating the graph of the preciseconstituents, the precise constituents are built traversally on thegraph 1440 of linear division via the left and right boundaries of thecore constituents. For each built path on the graph 1440 of lineardivision, the set of syntforms is determined; linear order is checked(verified) and rated for each of the syntforms. Accordingly, a preciseconstituent is created for each of the syntforms, and the building ofprecise child constituents is recursively initiated.

When a precise child constituent is built, an attempt is made to attachthe precise child constituent to the precise parent constituent. Whenattaching child constituents, restrictions which the child constituentsimpose on the set of meanings of a parent constituent are taken intoaccount, and the upper lexical rating of the link is calculated. Whentrying to attach each child constituent, two types of restrictions,which are represented by means of bit masks, are formed: the restriction(mask) on grammatical values of the parent constituent, which isreceived with the help of the agreement rule, and the restriction (mask)on grammatical values of the child constituent, which is received withthe help of the agreement or government rule. For each description of adeep slot which may have diathesis correspondence to the current surfaceslot, the following restrictions are obtained: the restriction on thelexical meanings of the parent constituent, the restriction on thepossible lexical meanings of the child constituent and the restrictionon the preferred lexical meanings of the child constituent (the set ofpreferred semantic classes in the description of the deep slot).Additionally, deep rating is obtained as a degree of conformity of thedeep slot with these restrictions.

If there is a suitable identifying word combination in the sentence, forexample, an idiom, which meets the restriction on parent lexicalmeanings, the rating of the word combination is added to the deeprating. If none of the lexical meanings of child constituent meets thedeep restrictions of this deep slot, attachment to this deep slot isimpossible. The possibility of attachment to the other deep slots ischecked. A deep slot which has the maximal value of the deep rating isselected.

The masks of grammemes for all child constituents which could beattached are merged. The mask on grammatical values of the parentconstituent is used for calculating its grammatical value. For example,when child constituents are attached, the grammatical value of thesyntactic form according to its correspondence with the childconstituents is defined more precisely.

Coordination is also processed when a child constituent attached duringthe stage 1450. For slots filled by coordination, there exists a need tocheck that not only the apex of coordination can be attached but itsother components as well.

Additionally, ellipsis is also processed when a child constituentattached during the stage 1450. Surface slots which are required in thesyntform and do not permit ellipsis may be empty. In this case, whengenerating a precise constituent, a proform is placed in the empty slot.

As result of the stage 1450, the graph of the precise constituents 1430,which covers the whole sentence, is built. If the stage 1450 forgenerating the graph of the precise constituents has failed to producethe graph of the precise constituents 1430 which would cover the entiresentence, a procedure which attempts to cover the sentence withsyntactically-separate fragments is initiated. In this case, adummy(fictitious) generalized constituent is generated, where allgeneralized constituents of the sentence may be attached.

As shown in FIG. 14, when the graph of precise constituents 1430, whichcovers the sentence, was built, one or more syntactic trees can begenerated at the step of generating 1460 during the precise syntacticanalysis 540. Generating 1460 of the syntactic trees allows generatingone or more trees with a certain syntactic structure. Since surfacestructure is fixed in a given constituent, adjustments of structuralrating scores, including punishing syntforms which are difficult or donot correspond to the style, or rating the communicative linear order,etc., may be made.

The graph of precise constituents 1430 represents several alternativesaccording to different fragmentation of the sentence and/or differentsets of surface slots. So, the graph of precise constituents representsa set of possible trees—syntactic trees, because each slot can haveseveral alternative fillers. The fillers with the best rating may form aprecise constituent (a tree) with the best rating. Thus the preciseconstituent represents unambiguous syntactic tree with the best rating.At the stage 1460, these alternatives are searched and one or more treeswith a fixed syntactic structure are built. Non-tree links in the builttrees are not defined yet. The result of this step is a set of bestsyntactic trees 1420 which have the best rating values.

The syntactic trees are built on the basis of the graph of preciseconstituents. For these precise constituents, syntactic forms, theboundaries of the child constituents and the surface slots aredetermined. The different syntactic trees are built in the order ofdescending of their structural rating. Lexical ratings cannot be fullyused because their deep semantic structure is not defined yet. Unlikethe initial precise constituents, every resulting syntactic tree has afixed syntactic structure, and every precise constituent in it has onlyone filler for each surface slot.

During the stage 1460, the best syntactic tree 1420 may generally bebuilt recursively and traversally from the graph 1430 of preciseconstituents. The best syntactic subtrees are built for the best childprecise constituents, syntactic structure is built on the basis of thegiven precise constituent, and child subtrees are attached to thegenerated syntactic structure. The best syntactic tree 1420 can bebuilt, for example, by selecting a surface slot with the best qualityamong the surface slots of a given constituent and generating a copy ofa child constituent whose sub-tree is the best quality sub-tree. Thisprocedure is applied recursively to the child precise constituent.

On the basis of each precise constituent, the best syntactic tree with acertain rating score can be generated. This rating score can becalculated beforehand and specified in the precise constituent. Afterthe best syntactic tree is generated, a new precise constituent isgenerated on the basis of the previous precise constituent. This newprecise constituent in its turn generates a syntactic tree with thesecond-best value of the rating score. Accordingly, on the basis of theprecise constituent, the best syntactic tree may be obtained, and a newprecise constituent may be built.

For example, two kinds of ratings can be kept for each preciseconstituent during the stage 1460, the quality of the best syntactictree which can be built on the basis of this precise constituent, andthe quality of the second-best syntactic tree. Also, the rating of theprecise constituent includes the rating of the best syntactic tree whichcan be built on the basis of this precise constituent.

The rating of a syntactic tree may be calculated on the basis of thefollowing values, but not limited to, structural rating of theconstituent; upper rating for the set of lexical meanings; upper deeprating for child slots; ratings of child constituents, etc. When aprecise constituent is analyzed to calculate the rating of the syntactictree which can be generated on the basis of the precise constituent,child constituents with the best rating are analyzed in every surfaceslot.

During the stage 1460, rating calculation for the second-best syntactictree differs in some ways including, but not limited to, for one of thechild slots, its second-best child constituent is selected. Anysyntactic tree with a minimal rating loss relative to the best syntactictree may be selected during this stage 1460.

When the stage 1460, additional restrictions on constituents may betaken into account. Each precise constituent which gets into the besttree may be checked for additional restrictions. If a constituent or oneof its child constituents does not meet the restrictions, theconstituent may receive a mark that its best tree does not meet theadditional restrictions. A check may be performed to determine whetherthis subtree meets the additional restrictions.

The rules of additional restrictions are checked during the stage 1460to make sure whether a constituent meets the restrictions but alsosuggest the steps which should be taken in certain slots so that theconstituent will meet the restrictions. This approach can alsosignificantly increase task-orientation of the search. The restrictionsused during the stage 1460 can be defined for any surface slot and thecorresponding deep slot. On the basis of the specified restrictions, thedifference in quality between the best and second-best tree for thissurface slot is calculated. As a result, a generation method is providedwhereby a tree which meets the additional restrictions can be found assoon as possible.

Near the end of the stage 1460, a syntactic tree with a fully-definedsyntactic structure is built, i.e. the syntactic form, childconstituents and surface slots that they fill are defined. Since thistree is generated on the basis of the best hypothesis about thesyntactic structure of the initial sentence, this tree is called thebest syntactic tree 1420. The returning back 1462 from generating 1460the syntactic trees to the building 1450 of the graph of preciseconstituents is provided when there are no syntactic trees withsatisfactory rating generated, or the precise syntactic analysis isunsuccessful.

FIG. 15 illustrates schematically an exemplary syntactic tree accordingito one embodiment of the invention. In FIG. 15, constituents are shownas rectangles, arrows show filled surface slots. A constituent has aword at its core (Core) with its morphological value (M-value) andsemantic parent (Semantic class) and can have smaller constituents ofthe lower level attached. This attachment is shown by means of arrowsnamed Surface Slot. Each constituent has also a syntactic value(S-value), expressed as the grammemes of the syntactic categoriesthereof. These grammemes are the properties of the syntactic formsselected for the constituent during the precise syntactic analysis 540.

FIG. 16 is an example of syntactic tree of the above mentioned sentence“This boy is smart, he'll succeed in life.” This syntactic tree is firstgenerated as a result of stage 1460 of generating syntactic trees of theprecise syntactic analysis 540 performed on the graph 1300 of thegeneralized constituents shown in FIG. 13, and can be represented as asubgraph of the graph 1300 of the generalized constituents, according toone or more embodiments of the invention.

A rectangle shows a constituent with the selected lexical meaning of thecore and its morphological paradigm in broken brackets, for example,Verb or Noun&Pronoun. The root of the syntactic tree 1600 is aparticular value #NormalSentence, which serves as a clause value. Thearrows are marked by the names of the surface slots, such as Modal,Verb, Subject, Demonstrative, etc., and for some of the surface slots,the corresponding rating scores are shown.

During the stage 1470, non-tree links are specified for the bestsyntactic tree 1420. Since, as a rule, non-tree links appear on thesyntactic tree, and it is not a tree anymore, it is called a syntacticstructure after the stage 1470. Since many different non-tree links maybe specified, several syntactic structures with defined non-tree links,i.e. with a fully-defined surface structure, may be obtained. The stage1470 may result a syntactic structure 1402 with the best rating—the bestsyntactic structure. During the stage 1470, proforms are inserted intothe best syntactic tree 1420, non-tree links are specified, such as byperforming ellipsis description 852 and coordination description 854.Additionally, the grammatical agreement between each element of thesentence, which may be as a relationship of control, for example, acontroller and a controlled element, using the referential andstructural control description 856, is checked. Additionally, syntacticstructures 1475 of previous sentences may be used.

Non-tree links are established on the best syntactic tree 1420—the treeof constituents with unambiguously fixed fillers of child slots.However, during the stage 1470, many different non-tree links for thesyntactic tree, which may be the best at the current moment, can begenerated. Accordingly, several different syntactic structures withnon-tree links may be built for each syntactic tree. These syntacticstructures or syntactic structure variants generated from differentsyntactic trees may vary in the inserted proforms, their positions inthe tree, and non-tree links. To be able to define an antecedent in theprevious text, several of the syntactic structures 1475 of previoussentences from the previous syntactic analysis can be saved. Thesyntactic structure with the best rating is selected as the bestsyntactic structure 1402. If the stage 1470 is unsuccessful, thereturning back 1472 to the stage 1460 is provided to obtain thenext-best syntactic tree 1420 with the next value of rating score.

Many other syntactic trees may be generated during precise syntacticanalysis 540. These trees can be generated one after another, while thestage 1470 to generate non-tree links on the previous syntactic tree isunsuccessful. The difference between these syntactic trees lies in theirstructures, filled surface slots for some constituents, and/or themorphological paradigms for some constituents. For example, during theprecise syntactic analysis 540 of the above mentioned sentence “This boyis smart, he'll succeed in life.” the stage 1470 was unsuccessful on thetree 1600 and some other syntactic trees. FIG. 17 is one of syntactictrees for the sentence extracted from the graph of generalizedconstituents from FIG. 13, it is the first from generated trees whicheventuate successfully of the stage 1470. So, the tree 1700 isconsidered as the best syntactic tree.

FIG. 18 is one example of a syntactic structure 1402, which is obtainednear the end of the stage 1470 for the sentence “This boy is smart,he'll succeed in life.” with non-tree links generated on the basis ofthe best syntactic tree which is shown on FIG. 17. A non-tree link oftype “Anaphoric Model—Subject” 1810 is established from the constituent“boy” 1820 to the constituent “he” 1830 to identify the subjects of thetwo parts of the complex sentence. Additionally, a proform PRO 1840 isinserted to establish a link between the controller (“boy”) 1820 and thecontrolled element (“smart”) 1850. As a result, the complement “smart”1850 fills the surface slot “Modifier_Attributive” 1860 of thecontroller “child” 1820 by means of a link of type “Control-Complement”1870.

During the stage 1470, proforms are inserted. For every element of thesentence which can be a controller, its own proform is inserted. If apronoun (or a proform substituted during the rough syntactic analysis)is controlled, a copy of the pronoun is uniformly made. As a result,every controlled element has a single controller. A controller can haveseveral controlled element variants as different alternatives. Ideally,all available proforms are inserted. However, in the final syntactictree, there may be only one of the control element variant remained. Inaddition, the set of meanings for a controlled element may be calculatedfrom the controller; for example, a set of lexical meanings may be takenfrom the controller, a set of grammatical values may be limited by theagreement rule, etc. In general, the initial mask of a proform resultsin all the available meanings, whereas the initial mask of a pronoun maypermit some meanings, e.g., as restricted by the morphological form ofeach element of the sentence. For example, after checking with agreementrules, the mask of a pronoun can be empty such that any linking orpairing up between the controller and its proform cannot be established.For example, in some cases, the gender of the controller and the pronounmay not agree; in these cases, only limited numbers of proformsinserted.

At the stage 1470, the possibility to attach the controlled element tothe surface slot is determined in a similar way as in attaching a childprecise constituent in order to narrow the numbers of the qualifiedmeanings of the controlled element. In general, the parent constituentmay be left unchanged for a period of time without changing itsgrammatical value, and the lexical meaning of the parent constituent maybe checked again at a later stage. Similarly, the controller may not bemodified until a later stage.

The referential and structural control description 856 contains ruleswhich can generate several alternative controlled elements during thestage 1470. The search for controlled elements can be organized as acall of all the rules in the slots of the syntactic tree which havealready been filled. Proforms may be sorted by their quality rating.Proforms which were substituted during the rough syntactic analysis buthave not received a controller can be deleted from the syntacticstructure.

During the stage 1470, for every syntactic tree, a best syntacticstructure with attached non-tree links can be generated, as a result. Ifno valid non-tree links have been generated, the syntactic structure ofthe best syntactic tree 1420 may be invalid. In this case, thesecond-best syntactic tree 1420 may be analyzed. If non-tree links havenot been successfully established, a returning back 1472 to the stage1460 is provided to obtain the next syntactic tree, which may have adifferent rating score, for generating anther syntactic structure withnon-tree links as the best syntactic structure. If none of the returningbacks 1462 and 1472 for the precise syntactic analysis 140 issuccessful, the returning back 544 to the rough syntactic analysis 530is provided. Additional rough syntactic analysis 530 can be performedwith additional consideration of any syntforms which may not have beenanalyzed previously.

As a result of the rough syntactic analysis 530 and the precisesyntactic analysis 540, the syntactic structure with specified surfaceand deep slots is built. There may be some ambiguity left in grammaticalvalues. The syntactic structure represents a full syntactic analysis ofthe sentence, indicates its surface and deep slots, and lexical meaningswhich have been unambiguously selected by this stage. Presence ofnon-tree links in the sentence determines, in the general case,generation of several different final structures according to differentvariants of establishing non-tree links. Final syntactic structures aresorted in the order of descending rating.

FIG. 19 illustrates a best syntactic structure 1900 with semanticparents of lexical meanings and their grammemes generated for thesentence “This boy is smart, he'll succeed in life.” during the precisesyntactic analysis 540. The best syntactic structure 1900 containsnon-tree links 1930 and 1940, the lexical meanings 1012 with semanticclasses as their semantic parents (1002), and their grammatical values1008. The semantic parents of the lexical meanings are shown by means ofa colon and capital letters, for example, “life:LIVE”. Grammaticalvalues are displayed in broken brackets. Because the deep slots havealready been determined in the end of precise analysis 540, instead ofthe surface slots the corresponding deep slots are displayed in FIG. 19:Agent, Locative, Agent, etc. To identify the elements “boy” 1920 and“he” 1930 by means of the non-tree link 1930, as it was displayed inFIG. 19, the element “boy:BOY” 1910 is copied to the element 1920,keeping the morphological value “Pronoun” in its grammatical value.

Semantic Analysis

As shown in FIG. 5, the semantic analysis 550 is performed after precisesyntactic analysis 540 when one or more the syntactic trees are formedand the best one with the highest rating score found. FIG. 20 is anexemplary process flow diagram illustrating the semantic analysis 550according to one or more embodiments of the invention. During semanticanalysis 550 a semantic structure 2002 of the source sentence 112 isbuild. The resulting semantic structure 2002 of the source sentence 112is built from the best syntactic structure 1402 according to variousapplicable analysis rules. Constituents for the semantic structure 2002are constructed by applying diathesis correspondences between thesurface (syntactic) and deep (semantic) slots of the constituents fromthe syntactic structure 1402 and by applying the rules of semanticinterpretation of the grammatical values of the constituents against aset of semantemes of various semantic categories. In one aspect, thesemantic structure 2002 includes a tree of deep constituents, each deepconstituent having one semantic class.

The language-independent semantic structure 2002 is generated during thesemantic analysis 550 using the diatheses 817, the deep models 912, theanalysis rules 860 (such as semanteme calculation rules 862 andnormalization rules 864), semantic descriptions 504 and lexical meaningsdescriptions 503 of the source language as well as pragmatic context2044 (as part of pragmatic descriptions 940) and communicativedescriptions 880. The semantic analysis treats the syntactic structureof a sentence in any language as a surface representation of alanguage-independent semantic structure.

A semantic structure 2002 is built from the selected syntactic structure1402 by performing steps 2010, 2020, 2030 of generating semanticstructure, calculating communicative semantemes, and normalizating andcalculating semantemes, among others. The syntactic structure 1402 asthe input data of the semantic analysis 550 may include specified deepslots and selected lexical meanings, the semantic structure 2002 may begenerated by substituting each lexical meaning in the source languagewith its language-independent semantic class and confirming the linearorder of the all the lexical meanings. Once the linear order isconfirmed, the surface slots can be deleted when generating the semanticstructure 2002 since only the deep slots 914 and deep slotsdescriptions, etc., are remained during the building of the semanticstructure 2002.

During the semantic analysis 550 to transform the syntactic structure1402 into the semantic structure 2002, deep correspondences forstructural elements of the syntactic structure 1402 are established, thegrammatical values of the constituents from the syntactic structure 1402are interpreted against semantemes to represent language-independentsemantic meanings, each lexical meaning is substituted with itslanguage-independent semantic class, and semantemes with semanticfeatures are generated. The resulting semantic structure 2002 is a tree(containing established non-tree links), with language-independentsemantic classes as nodes and a set of semantemes and deep slots asbranches.

During the step 2010, the semantic structure 2002 is generated from thebest syntactic structure 1402 using the semantic descriptions and thelexical descriptions 503, and the diathesis correspondences 817 betweenthe surface slots 815 and the deep slots 914 for each constituent of thesyntactic structure.

At the step 2020, communicative semantemes for constituents in thesemantic structure 2002 are calculated using semantemes calculatingrules 862 and communicative descriptions 880. The semantemes calculatingrules 862 can be used to semantically interpret the grammatical valuesof the constituents against a set of semantemes of various semanticcategories. Once the communicative semantemes are calculated at step2020, all other semantemes can be calculated, replacing grammemes withthe resulting calculated semantemes. The communicative semantemes areused to express the communicative properties of a sentence, such as thestandard linear order, the inverse linear order of a relative clause, orthe linear order of an interrogative sentence.

At the step 2030 semantemes are normalized and further calculated. Thepragmatic context 2044 and the analysis rules 860, such as thesemantemes calculating rules 862 and normalization rules 864, may beused during semantemes normalization to remove language asymmetries. Thesemantic normalization rules 864 are applied to remove languageasymmetries. For example, “all of any of the following functions” can benormalized to “all of the following functions”. As another example,“each of all of us” can be normalized to “each of us”. As still anotherexample, “He can do it, can't he?” can be normalized to “He can do it.”;since the deep slot of TagQuestion is filled and saved in the semanticstructure, the constituents “can't he” are removed.

The semantic normalization rules 864 are lexicalized and linked tospecific semantic classes and lexical meanings. There are two types ofthe semantic normalization rules 864: rules to be used prior tocalculating the semantemes for generating the semantic structure 2002;rules to be used after calculating the semantemes. A semantic class isconnected with ordered lists of transformation rules of the first andsecond type. Thus, the semantic normalization rules 864 can be usedprior to calculating the semantemes and after calculating the semantemesusing the respective semantic normalization rules 864.

In general, rules used during the semantic analysis 550 are applied tothe constituents of the semantic structure 2002 from the top down, froma parent constituent to child constituents. A constituent is analyzedwith rules connected to the semantic class of its core, in the order ofdescription. Rules connected with a certain class are used for all itschildren. In a child class there is a possibility to re-define inheritedrules: add new rules, change the order of application, forbid inheritedrules, etc.

The normalization rules 864 are applied to the semantic structure andmodify it. Some of the semantemes calculating rules 862 may be usedcyclically as long as their conditions are met. Use of semantemescalculating rules 862 leads, in particular, to substitution oflanguage-dependent characteristics, grammemes, with universalcharacteristics—semantemes.

When the semantemes for different constituents are calculated at thestep 2030 of normalizating and calculating semantemes, an additionalprocedure may be used. A semantemes calculating rule can check thepresence of certain semantemes of other constituents. Such a rule canonly work after all the semantemes which are specified in this rule havebeen calculated. To cope with this situation, the rules are started fromthe child constituents to the parent constituents. If a productionrefers to constituent semantemes which have not yet been calculated, therule stops with a special value which says that the rule completion mustbe postponed. A traversal of the tree from the top down is made,starting the rules which were postponed at the first stage. Once again,a traversal of the tree from the child constituents to the parent ismade by starting the rest of the postponed rules.

The result of the semantic analysis 550 is the semantic structure 2002of the source sentence built from the best syntactic structure 1402according to rules for the semantic analysis 550. A semantic structure,unlike a syntactic structure, uses universal language-independentconcepts and components, such as semantic classes, semantemes, deepslots, among others.

As shown in FIG. 20, a dispatcher 2040 for dispatching semanteme rulesis adapted to execute the normalization of the semantic structure 2002and calculating semantemes by applying the analysis rules 860. As aresult, every lexical meaning in the semantic structure 2002 issubstituted with its universal parent—a semantic class. Any possibledifferences of the child lexical meanings are saved in a list semantemesgenerated during the application of the analysis rules 860. Adescription of a constituent in the final semantic structure 2002includes semantic classes which are parents for lexical meaningsrepresented in the best syntactic structure 1402, semantemes which arecalculated according to the analysis rules 860 or assigned tocorresponding parent semantic classes, and child constituents. Whenthere is a link to a child constituent, the deep slot that can be filledis specified. The semantic structure 2002 is language-independent andmay include, but is not limited to, a tree of deep constituents, deepconstituents, and semantic classes which are the fillers of deep slots.Accordingly, the semantic structure 2002 can be applied to describe themeanings of a sentence from any natural or artificial languages.

FIG. 21 illustrates an exemplary resulting semantic structure 1700 ofthe sentence “This boy is smart, he'll succeed in life.” The deepconstituents are represented by rectangles with a semantic classindicated inside, for example, DECLARATIVE_MAIN_CLAUSE, TO_SUCCEED, BOY,LIVE, etc. The semantemes which are calculated after applying theanalysis rules 860 are displayed in broken brackets for each semanticclass. For example, <Imperfective, Realis, Indicative, Present> is thesemantemes for the semantic class BE 2110. Some of the applied analysisrules are displayed near rectangles with the semantic class. Deep slotsare represented as arrows and named; for example, Object, Agent,Locative, etc. Non-tree links are represented as dotted arrows.

Natural Language Sentence Synthesis

FIG. 22 illustrates one example of a method 2200 exemplifying processflow diagram of synthesis 560 of an output natural language sentence.The step is illustratively described below can be configured togenerating a surface syntactic structure of a sentence in an outputlanguage from a language-independent semantic structure, such as alanguage-independent semantic structure generated after analyzing asource sentence in a source language. However, it should be understoodthat the invention has utility in other system configurations, such asother computer systems, algorithms, and any other data processingsystems, including those systems configured to analyze, generate, and/ortranslate language sentences and language descriptions.

The method 2200 for generating a natural language sentence 114 in anoutput language may include a step 2220 of performing a lexicalselection on a semantic structure 2002, a step 2240 of building asurface structure 2204 of a sentence to be generated from the semanticstructure 2002 with selected lexical meanings, a step 2260 of restoringmovements and determining linear order on the surface structure 2240,and a step 2280 of performing morphological synthesis on the surfacestructure 2204 in the output language.

For a semantic structure 2002, the lexical selection 2220 and building2240 a surface structure are performed, and for the obtained surfacestructure 2204 of a sentence to be generated restoring 2260 movementsand determining the linear order are performed, and the morphologicalsynthesis 2280 is executed to generate the output sentence 114 in anynatural language, for which all the necessary language descriptions havebeen created. The output sentence must express that meaning (sense) inthe given natural language, which is represented by the sourcelanguage-independent semantic structure. All these method steps may beperformed by the methods, software, algorithms, computer systems,computer-readable media, and devices according to embodiments of theinvention. For example, each of these method steps thereof can beadapted to be stored as software, algorithms, and computer-readablemedia, or alternatively, within computer systems and devices. As anotherexample, one or more algorithms, computer systems, or subsystems can beused to perform one or more method steps as described in FIG. 22.

Lexical selection 2220 is selecting one or more lexical meanings for adeep constituent core. Any constituent has a word at its core and caninclude child constituents at lower levels. As a rule, the grammatical,syntactical and morphological properties of the deep constituent,expressed by means of a set of semantemes, are the same as theproperties of its core. At the step of the lexical selection 2220 in thesemantic class of the core the lexical class of the target language isselected. As lexical meanings 1012 in lexical description 503 have theirsemantic values 1010 which are also expressed by means of a set ofsemantemes, and those lexical meanings in the semantic class areselected, which have a most number of semantemes of the constituentcore.

Also, deep models 912 as a part of semantic description 504 are used atthe step of the lexical selection 2220, because semantic classes of thefillers of the child and parent deep slots are taken into account.Accordingly, those lexical meanings in the semantic class are selected,which have deep slots, and those semantic classes of deep slot fillersin their deep models 912, which correspond to the deep slots andsemantic classes of deep slot fillers of the constituent core.

As a rule, for the cores only those lexical meanings are selected whichare linked to the semantic class in the semantic hierarchy 910 by meansthe relation of mutual semantic representability. The mutual semanticrepresentability means that it is possible the transition not only fromthe lexical meaning to the semantic class, but from the semantic classto the lexical meaning. Any semantic class always has at least one suchlexical meaning-representative in the given natural language.

Additionally, various ratings 2222 may influence on the lexicalselection 2220, such as, rating of the lexical meaning, rating of thedeep slots filling, ratings of identifying word-combinations, ratings ofdeep slots correspondences, bonus for derivational semantemes, rating ofcorrespondence to the local and global pragmatic context, rating ofcorrespondence to the terminological sphere, rating of correspondence tothe previous selection. Pair ratings may take into account not only therelations between the parent and child constituents but non-tree linkstoo. Since there may be many lexical meanings meeting the conditions oflexical selection 2220, lexical meanings having a best rating areselected at first.

As a rule, at the step of lexical selection 2220 among lexical meaningsactually the one (or more) is selected, which realizes the mostsemantemes assigned to the constituent core, on the basis of ratings2222 of lexical meanings and ratings of pair correspondence. There arecases, however, when the rules 2224 of lexical selection and structurecorrection have to be used. These rules are used when the semanticstructure needs correction in order to overcome the asymmetries betweenthe universal semantic description and the language-specific syntacticstructure. Rules 2224 of lexical selection and structure correction areconnected with deep slots 914 and transform a sub-tree with the currentconstituent at the top. During this process the rules can substitute anew parent constituent.

The semantic structure correction rules may be used during the lexicalselection in the case when the algorithm of selection of the lexicalmeaning for a semantic class cannot be described with the standardmeans, for example, during the lexical selection the system has to takeinto account the deep slot to be filled, or the semantic class of thechild constituent, etc. For example, the rule for the English languagenamed SingleChoice, containing the instructions: <<Elective>>=>“singleone:SOLE”; <<ZeroElective>>=>“single:SOLE”; allows the system to make aselection of the lexical meaning “single one” from the semantic class“SOLE” if the semanteme of electiveness is assigned to the constituent,or of the lexical meaning “single” if this semanteme is not assigned.Since the category of electiveness is calculated and not assigned in thelexical description 503, this selection condition cannot be described inthe semantic hierarchy 910 but can only be specified with the help ofthe rule 2224 of lexical selection and structure correction.

As another example, when the rules 2224 of lexical selection andstructure correction may be used, the deep structure of a certainlanguage differs from the “normalized” language-independent structure,for example, it has an additional constituent or a different directionof government between its constituents, etc. For example, suppose auniversal semantic structure has a constituent which has the semanticclass “NEWS” as its core and a child constituent filling the “Quantity”deep slot. In order to synthesize the syntactically and stylisticallycorrect English phrase “two pieces of news”, the following structurecorrection rule may be used: “NEWS” [Quantity: x, ?y]=> new“piece:CLASSIFIER” [QuantifiedEntity:this][x][y], which transforms thetree in such a way that the parent constituent has the piece:CLASSIFIER”lexical meaning, while “NEWS” becomes the child constituent and fillsthe QuantifiedEntity slot of the parent constituent.

If the rules 2224 of lexical selection and structure correction areapplied, the lexical selection 2220 may entail the transformation of thesemantic stricture 2002; besides, the rule may change the deepproperties of some constituents, such as, semantic value, semantic classof the core, etc. These rules are lexicalized, i.e. they are connectedwith (assigned to) certain objects of the semantic hierarchy 910 and areonly called when such an object is the core of the initial constituent.

As a result, during the lexical selection 2202 the source semanticstructure 2002 may be transformed and each constituent has one or morelexical meaning selected for its core. On such specified semanticstructure 2002 with specified lexical meanings of the constituents thebuilding 2240 the surface structure is performed. As shown on FIG. 22,various specific language descriptions, such as, syntactic description502, referential and structural control description 856, grammemessynthesis rules 2242, alternative realization rules 2244, and agreementrules 2246, among others, may be used during the building 2240 thesurface structure.

The surface structure 2204 is built by means of a top-down traversal ofthe semantic structure. During this traversal, semantic, lexical andsyntactic properties of each constituent are specified more accurately,and, first of all, the surface slots corresponding to the deep slots aredetermined, the linear order is defined, movements are restored,structural and referential control are checked.

FIG. 23 is a diagram schematically illustrating the idea of a surfacestructure 2300 of a synthesized sentence according to one or moreembodiments of the invention. In FIG. 23, constituents of the surfacestructure 2204 are shown as rectangles, arrows show filled surfaceslots. A constituent has a lexical meaning at its core with its semanticparent (SEMANTIC CLASS) and can have smaller constituents of the lowerlevel attached in some surface slots. This attachment is shown by meansof arrows named Surface Slot. Each constituent may also includesyntactic values and grammatical values, expressed via the grammemes ofthe syntactic categories thereof. These grammemes are the properties ofthe syntactic forms selected for the constituent during the building2240 the surface structure.

Since any lexical meaning 1012 in its lexical description 503 has a deepmodel 912 and a surface model 810 connected by means of diatheses 817,for each lexical meaning corresponding to the constituent core, thefollowing actions may be performed. For each deep slot of the parentconstituent in its diathesis all surface slots are searched for whichmeet the diathesis restrictions. At least one slot must be found. If noslot has been found, the returning back 2230 to the stage of lexicalselection 2220 is provided, and the lexical meaning which has thenext-best rating in the semantic class is selected.

Since there may be many surface slots 815 meeting the conditions ofdiatheses 817 for each lexical meaning 1012, each of these surface slotsmay be considered as a hypothesis related to a surface structure of acorresponding constituent. Such hypothesis may have a rating. Thosehypotheses that may result in a best rating are served at first. Foreach surface slot 815, syntactic forms 812 which meet the requirementsof the surface slot are searched for. If a suitable syntactic form hasnot been detected, this hypothesis is penalized by means of ratingreduction. An additional rating for the correspondence of the semantemesof the part of speech and the grammatical type to the correspondinggrammemes of syntform 812 for each hypothesis is calculated.

The hypotheses about surface structure of a constituent are analyzedduring building 2240 the surface structure in the order of descendingrating. If a suitable syntactic form for an analyzed hypothesis isn'tfound, an alternative realization rule 2244 may be applied. Such rule isapplied if the lexical meaning which during lexical selection 2220 isselected hasn't suitable grammatical forms. Alternative realizationrules 2244 usually substitute some semantic class as the parentconstituent and/or transform the semantic structure 2002 what enable tobuild the surface structure with another lexical meaning.

Alternative realization rules 2244 are lexicalized, i.e. they areconnected with (assigned to) certain objects of the semantic hierarchy910 and are a part of lexical description 503. If some alternativerealization rule 2244 was applied and its application has resulted inthe substitution of a semantic class or a new lexical meaning as theparent constituent, this hypothesis is removed from the queue ofhypotheses, all the previous step (searching for syntax forms) arerepeated with it, and thus new hypotheses are generated. Thesehypotheses are added to the list of hypotheses, and ratings of thehypotheses are taken into consideration. The repeated lexical selectionis performed as follows: the lexical meanings which have a syntacticform which is suitable for the parent surface slot, and the lexicalselection 2220 in the sub-tree of this constituent is started.

During building 2240 the surface structure, grammemes synthesis rules2242 are applied. Grammemes synthesis rules 2242 calculate grammemes,representing grammatical and morphological values of a constituent, onthe basis a set of semantemes, taking into account the initialgrammatical value of the lexical meaning, parent surface slot andsyntactic form. As a result of these rules applying, semantemes may besubstituted by grammemes. Generally, this rules may have a productionform, a left part of the rule describing a condition of the ruleapplying—one or more semantemes and, additionally, surface slot name,which a constituent must have, and a right part of the rule containingone or more grammemes, which the constituent are assigned as result ofthe rule applying. As the order of the grammemes synthesis rules 2242applying may be determined by presence not only some semantemes, butgrammemes too, so, not only semantemes but, additionally, grammemes maybe included in the condition of a rule applying.

The grammemes synthesis rules 2242 allow the system to detect agrammatical value of the lexical meaning which realizes as manysemantemes as possible, and to calculate the value of all semanticgrammatical categories. Each applied rule determines more accurately thegrammatical meaning of the constituent as it is written in the appliedproductions. If a production tries to assign to a constituent agrammatical value that contradicts the value that the constituentalready has, such a production will not work even if its requirement ismet by the current constituent.

Semantemes may be realized not only grammatically but also lexically,namely by substitution of the parent or child constituents. Somesemantemes may be realized only by means of substituting auxiliary wordsinto the parent constituent, for example, modal or auxiliary verbs. Inthis case, the rule creates and substitutes a new parent constituent.The new parent constituent contains a semantic class which is attachedto a slot of the initial parent constituent. Additionally, the rule mustmove the semantemes which must be realized by the syntactic form of thesubstituted parent constituent (for example, aspect-tense) to the parentconstituent and delete these semantemes from the current constituent.The rule must attach the current constituent to the deep slot of thesubstituted parent constituent. For the semantic class of new parentconstituent the lexical selection 2220 in the sub-tree of thisconstituent is executed.

During building 2240 the surface structure, for each hypothesis aboutsurface structure of a constituent all syntactic forms which correspondto the calculated grammatical value are detected, and each hypothesis isconsidered separately for each detected syntactic form. Each separatedin such a way hypothesis is verified according to morphologicaldescription 501 whether the core of this constituent can be synthesizedon the basis of its partially calculated grammatical value. If thiscannot be done, the hypothesis is deleted. A preliminary rating for eachhypothesis is calculated and they are arranged in the order ofdescending rating scores.

The syntactic forms are analyzed in the order of descending rating. If aconstituent has deep slots which are filled without diathesiscorrespondence, a corresponding alternative realization rule 2244 iscalled for each such slot. This algorithm may be performed recursivelyfor each child constituent. During backward recursion the structurecontrol rules (they are a part of referential and structural controldescription 856), related to the surface slots of this constituent, arechecked, and, if the control rule has not detected a suitable non-treelink, the constituent is deleted. Otherwise, the movements which aredescribed in the surface slots of this constituent are restored. If themovement cannot be restored, the constituent is deleted.

In the end of the considering of each hypothesis about surface structureof a constituent final rating of the hypothesis is calculated. If thefinal rating of the current hypothesis is higher than the preliminaryrating of the next hypothesis, the search is stopped. This algorithm ofbuilding 2240 the surface structure is a two-level search withindependent selection and filtering at each level. At the upper levelhypotheses are generated and assigned their ratings. These hypothesesconsist of three components: lexical meaning, surface slot, andsyntactic form. At the lower level hypotheses corresponding to specificsyntactic forms are analyzed. The best hypothesis is represented by abest surface structure, which is a tree (best surface tree), the nodesof which are constituents with selected lexical meanings andcorresponding syntax forms and the branches are the surface slots. As aresult of the step 2240, the surface structure of the sentence to begenerated with the best rating is build.

FIG. 24 is an exemplary best surface (syntactic) structure of theRussian sentence which is obtained as result of translating the Englishsentence “This boy is smart, he'll succeed in life.” according to oneembodiment of the invention on the basis of the semantic structure whichis shown on FIG. 21. Restoring 2260 movements and determining the linearorder is performed for the best surface structure. During this stepreferential and structural control is checked and movements arerestored. The relations of control may be represented in the surfacestructure by means of non-tree links. Some non-tree links may bedescribed in the semantic structure 2002, for example, in case, when thesemantic structure 2002 was obtained as result of analysis of somesentence. The movements may be represented in the surface structure bymeans of non-tree links too, or otherwise, corresponding non-tree linksmay be restored by means of special structural control rules.

A movement is a phenomenon of various natural languages. The movementswhich must be restored, may be of different types, such as,communicative movements (subject rise, cleft-constructions), stylisticmovements (object rise), grammatical movements (relativization,interrogatory sentences, etc.), among others. Accordingly, the differenttypes of movement may express different communicative or stylisticaspects, for example, to mark out the focus or emphasis of the sentenceto be generated. This may entail a modification of a linear order. As aresult, the sentence to be generated may be more colloquial and close toreal time situation and a natural language. For example, the sentence“John is a good boy and it seems that John loves Mary.” may begenerated, but “John is a good boy and seems to love Mary.” is more realand spoken, and the later may be generated through movement of “John”because of a co-ordination.

The other example of sentence which may be generated from thelanguage-independent semantic structure formally following the Englishlanguage rules is “I've met a boy my sister likes [whom].” This sentencemay be transformed into more usable variant “I've met a boy whom mysister likes.” by movement of “whom”.

The referential and structural control description 856 is used innon-tree links generation, during which proforms may be inserted,non-tree links may be established, and all rules of correspondencebetween the controller and controlled object are checked. Structuralcontrol check allows filtering out wrong surface structures. Therelations between the controlling constituent—controller—and theconstituent controlled by it are checks. For example, a verb attributeof a noun phrase can generally be expressed by a participial clause or arelative clause. This element (the verb attribute) is represented insurface structure by auxiliary element named a proform which is insertedby a structure control rule and may be controlled by the noun phrase. Ifthe controlled proform is related to the subject, both the variants arepossible, otherwise only a relative clause is possible. An attempt touse a participial clause in order to realize a verb attribute of a nounphrase in the control rule fails, and thus such a variant is discarded.Non-tree links which have not been interpreted by structure controlrules get interpreted by referential control rules at the correspondingproforms. Consequently, every lexical meaning connected with a proformmay have its referential control rule.

The non-tree links on the surface (syntactic) structure for the Russiansentence which is obtained as result of translating the above mentionedEnglish sentence “This boy is smart, he'll succeed in life.” accordingto one embodiment of the invention are shown on FIG. 24. The non-treelinks are shown as dotted arrows. These non-tree links may be kept inthe language-independent semantic structure, for example, in the casewhen this language-independent semantic structure was obtained as resultof analysis of the sentence in the same or another natural language. Inthe other case, the non-tree links may be restored according thereferential and structural control description 856. A non-tree link oftype “Anaphoric Model—Subject” 2410 is established from the constituent“Ma

:BOY” 2420 to the constituent “Ma

:BOY” 2430 to identify the subjects of the two parts of the complexsentence. Additionally, a non-tree link of type “Conjunction link” joinstwo parts of the complex sentence.

Additionally, determining precise values of relational grammaticalcategories is executed. The relational grammatical categories mayexpress grammatical properties of a child constituent, such as a gender,a number and so on, which depend on properties of the parentconstituent. The agreement rules 2246 are used for determining precisevalues of relational grammatical categories. Sometimes for a full andunambiguous determining a grammatical meaning, control rules have to betaken into account. For example, there is not enough information in thesurface structure 2204 to generate sentences “I met Mary with herdaughters.”, or “I met John with his daughters.”, or “I met the Smithcouple with their daughters.” In these examples the gender or number ofa possessive pronoun is determined by a controller (controlling element)therefore only control rules, which are included into referential andstructural control description 856, can determine values of thesecategories. Transforming a proform into a personal, reflexive orrelative pronoun, or into an empty proform is also performed at thisstage. It is done by means of assigning to the proform a correspondingrelational meaning by the control rule.

The linear order is determined after detecting relational grammaticalmeanings because they may affect the linear order (for example, the typeof a pronoun). At this stage the syntform 812 which has correspondinglinear order description 816 is already known. A communicative formwhich realizes communicative semantemes for the syntform must beselected on the basis of communicative description 880, and the order ofslots is synthesized. Communicative forms are searched in the order oftheir description. The first form which meets all the requirements andincludes all slots is selected. If the search has been failed to selecta suitable communicative form, a neutral order is synthesized.

The result of the stage 2260 is a fully defined (specified) surface(syntactic) structure 2204 of the sentence to be generated where foreach constituent a lexical meaning of the core, surface slots and theirfillers, and their linear order are specified according to syntacticdescription 502, referential and structural control description 856,communicative description 880, agreement rules 2246, among others. Thisstage 2260 and the previous lexical selection 2220 on the basis of rules2224 of lexical selection and structure correction allow the system toget the surface (syntactic) structure 2204, which express the semanticmeaning of the sentence to be generated in the given natural language asfully and precisely(exactly) as possible.

The morphological synthesis 2280 of the constituent cores is performedon the basis of the morphological description 501. The grammatical valueof a constituent core is determined on the basis of the grammaticalvalue of the constituent and the already-detected syntactic form. Eachsyntactic form may have a rule of agreement of the grammatical value ofthe constituent and the morphological value of the core. This agreementrule determines the morphological value of the core.

Prior to generating a word form with the help of the morphologicaldescription 501, a lexeme must be selected which corresponds to theselected grammatical value. It is necessary because each lexical meaningmay be associated with a set of lexemes which encode, for example,different dialects or even separate word forms of the lexical meaning.For example, the lexical meaning “cow” in English may be associated notonly with the lexeme “cow”, but with “bull” and “calf”, among others.The required lexeme may be selected according to the value ofgrammatical category “Gender”, and additionally, according to thepresence of semanteme “Baby”. The morphological value of the core issynthesized according to morphological grammemes, for example, for thenoun—according to the grammemes of the number, the case must be takeninto account in English, for the verb—the grammemes of the number,person, tense, participle type, among others. As a result of processsteps 2220, 2240, 2260 and 2280, a sentence in the output naturallanguage may be generated according to the language-independent semanticstructure. For above mentioned example, the result of translating theEnglish sentence “This boy is smart, he'll succeed in life.” accordingto one embodiment of the invention into Russian is the sentence “

TOT Ma

COC

pa

Te

e

, OH

oeyc

eeT

.”

The method and process flow as described herein can be adapted into oneor more computer-readable media or one or more algorithms in order toconvert a natural-language sentence into its language-independentsemantic structure and to convert a language-independent semanticstructure into an output natural-language sentence. Thecomputer-readable media or one or more algorithms may be adapted toperform a translation process which includes one or more thelexical-morphological analysis 520, the rough syntactic analysis 530,the precise syntactic analysis 540, and the semantic analysis 550, andthe building 560 the output sentence.

The one or more computer-readable media or one or more algorithms of theinvention can be implemented on one or more analyzers, devices, orcomputer systems, adapted to perform a single analysis or just a coupleof the analyses as described herein and linked together afterward. Thealgorithm of obtaining the semantic structure is fairly complex, asthere are ambiguities at each step, and from a multitude of parsingvariants only the most probable one is selected, based on the ratingswhich take into account semantic, stylistic and pragmatic factors andstatistical data. In turn, the algorithm of obtaining thenatural-language sentence on the semantic structure is complex too, asthere are, in turn, many ambiguities at each step, and from a multitudeof hypotheses only the most probable one is selected, based on theratings which take into account semantic, syntactic and pragmaticfactors and statistical data. The computer-readable media or one or morealgorithms may be adapted to perform lexical selection, building asurface structure, restoring movements and determining the linear order,and the morphological synthesis.

During each step shown in FIG. 5 the user of the computer system canview and, if necessary, select each of the interim and resultingstructures. By performing the lexical, morphological and syntacticanalyses of a sentence, a syntactic structure as a tree of generalizedconstituents can be established. The syntactic structure of a sentenceis transformed into a semantic structure by semantic interpretation oflanguage-specific elements of the syntactic structure of the sentenceand a tree of surface constituents are transformed into a tree of deepconstituents and a language-independent semantic structure is formed.During the building 560 of the output natural language sentence byperforming the lexical selection on the semantic structure, building asurface structure of the sentence to be generated in the given naturallanguage the syntactic structure as a tree of surface constituents canbe build. On the syntactic structure of a sentence movements arerestored and the linear order is determined, the morphological synthesisof the cores of constituents is performed to obtain the natural languagesentence.

A computer system implemented as a computer program with its owninterface or as part of another system in accordance with the method ofthe invention includes means for entering natural-language text; meansfor segmenting text into sentences, words, letters, and non-textsymbols; means for lemmatization and finding for each source word form acomplete set of its grammatical and lexical meanings; means forconstructing, in accordance with the model of each lexical meaning,constituents which are the realizations of these models in a givensentence; means for constructing one or more generalized constituentsfrom constituents constructed by using various models available for eachlexical meaning of a source word form; means for building a graph ofgeneralized constituents covering all the hypotheses about the possiblesyntactic structures of the sentence; means for calculating a roughrating of constituents which are included into generalized constituents;means for generating hypotheses about the most probable precisestructure of the sentence based on the rough ratings and for selectingthe structure with the highest value of the rating; means forcalculating the precise ratings for the selected, most probablesyntactic structure constituents which are included into generalizedconstituents; means for establishing non-tree links; means forestablishing correspondences for each surface slot of each constituentin the tree of constituents with deep slots; means for calculating theset of semantemes of each constituent on the basis of the set ofgrammemes; means for substituting each lexical meaning in the semantictree with its language-independent semantic class; means for storing ina database the constructed semantic structure for further use in otherapplications.

In the computer system, each element of the lexical structure isconsidered as a potential lexical core of the constituent. The means forconstructing a constituent may include means for determining all thepossible boundaries of the constituents; means for matching the surfacemodels of possible lexical meanings with selected fragments of a givensentence; means for initializing the surface models of possible lexicalmeanings.

In addition, the means for constructing generalized constituents fromconstituents constructed by using various models use data about the deepand surface models of the lexical meanings stored in a lexical-semanticdictionary may include means for generalizing surface models; means forgeneralizing deep models; means for constructing generalized diatheses.The means for building a graph of generalized constituents may include ameans for linking the constructed constituents to the surface slots ofthe parent constituents taking into account the linear word order.

Further, the means for calculating ratings for the selected syntacticstructure of a constituent are based on individual ratings of thelexical meanings, ratings of each of the syntactic constructions (e.g.,idioms, collocations, etc.) for each element of the sentence, and thedegree of conformity of the selected syntactic construction to thesemantic descriptions of the deep slots. The means for building a graphof generalized constituents includes means for filtering the constituentmodels being generalized.

The means for generating hypotheses about the most probable precisestructure of the sentence may include means for generating syntactictrees; means for generating the non-tree links; means for verifying themost probable hypothesis by generating specific hypotheses about thestructure of dependent constituents; means for choosing the bestsyntactic structure, i.e. for selecting the tree from the generalizedgraph. Further, the means for establishing non-tree links forcoordination processing, ellipsis, and referential relationships and themeans for substituting each lexical meaning in the semantic tree withits language-independent semantic class with registering distinctivesemantic features of the lexical meanings.

Further, the computer system in accordance with the method of theinvention includes means for storing and displaying a semanticstructure; means for the lexical selection of lexical meaning of thespecific language for each constituent core; means for correction ofsemantic structure in any specific natural language; means forselecting, in accordance with the model of each lexical meaning, surfaceslots and syntactic forms which realize the deep slots of the semanticstructure in the given specific language; means for calculating the setof grammemes of each constituent on the basis of the set of semantemes;means for an alternative realization of the lexical meaning by ananother semantic class; means for building the hypotheses about thepossible syntactic structures of the sentence; means for calculating arating of hypotheses about the possible syntactic structures of thesentence; means for selecting a structure with the highest rating value;means for restoring movements; means for determining precise values ofrelational grammatical categories; means for determining the linearorder on the basis of the communicative description; means for theselecting the grammatical forms; means for the morphological synthesis;means for displaying the obtained natural language sentence and storingit in a database for further use in other applications.

The means for calculating ratings for the selected syntactic structureof a constituent are based on individual ratings of the lexicalmeanings, ratings of each of the syntactic constructions (e.g., idioms,collocations, etc.) for each element of the sentence, and the degree ofconformity of the selected syntactic construction to the semanticdescriptions of the deep slots.

The means for generating hypotheses about the most probable surfacestructure of the sentence may include means for selecting surface slotsand syntax forms on the basis the set of semantemes to realize as manygrammemes as possible; means for verifying the hypotheses according tomorphological description; means for arranging and analyzing hypothesesin the order of descending rating; means for restoring movements. Also,the computer system can implement all the methods, steps, actionsautomatically.

FIG. 25 illustrates an example of a suitable computing systemenvironment on which the invention may be implemented. A system 2500 isprovided and is only one example of a suitable computing environment andis not intended to suggest any limitation as to the scope of use orfunctionality of the invention. The computing environment or system 2500should not be interpreted as having any dependency or requirementrelating to any one or combination of components as illustrated herein.

The system 2500 may be a general purpose computing device in the form ofa computer. Components of the system 2500 may include, but are notlimited to, a processing unit, such as a processor 2510, a system memory2520, and a system bus 2512 that couples various system componentsincluding the system memory 2520 to the processing unit 2510. The systembus 2512 may be any of several types of bus structures including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of bus architectures.

The system 2500 may generally include a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the system 2500 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage media(storage device) 2540 and communication media, such as an input device2550 and an output device 2560.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. which may performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices. In oneembodiment, various program applications, program modules, etc., such asa translation application 2530 are loaded into the memory 2520 and runby the processor 2510. The translation application may be adapted toperform the steps of the methods as described herein.

FIG. 26 illustrates another example of a system 2600 in accordance withone embodiment of the invention. The system 2600 may include aprocessing unit, such as a processor 2610, a memory 2620 and a networkinterface 2670. The memory 2620 may include a translation application2630 adapted to perform translation of a source sentence into an outputsentence using methods as described herein according to one or moreembodiments of the invention. The translation application 2630 may be,for example, a machine translation program for translating a sentencefrom an input language into an output language.

The memory 2620 may also include computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) andrandom access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within computer 2600, such as during start-up, is typicallystored in ROM. RAM typically contains data and/or program modules thatare immediately accessible to and/or presently being operated on by theprocessor 2610. These data and/or program modules are located in thememory 2620 or is loaded into memory when a program is called upon. Thenthe program is started and executed by the processor 2610 under thecontrol of an operating system. For example, RAM may contain theoperating system, various application programs, such as the translationapplication 2630, other program modules, and program data.

The system 2600 further include storage devices 2640 and/or variouscomputer storage media, including both volatile and nonvolatile,removable and non-removable storage media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, program modules or other data. Thestorage device 2640 includes, but is not limited to, RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile disks(DVD) or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by the system 2600. For example, the storage device 2640 mayinclude a hard disk drive that reads from or writes to non-removable,nonvolatile magnetic media, a magnetic disk drive that reads from orwrites to a removable, nonvolatile magnetic disk, and an optical diskdrive that reads from or writes to a removable, nonvolatile optical disksuch as a CD ROM or other optical media. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary operating environment include, but are not limited to,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, and the like.

Communication media typically embodies computer readable instructions,data structures, program modules or other data in a modulated datasignal, such as a carrier wave or other transport mechanism and includesany information delivery media. The modulated data signal may includesignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media may include wired media such asa wired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer readablemedia.

A user may enter commands and information into the system 2600 throughinput devices 2650, such as a keyboard 2658, a microphone 2656, ascanner 2654 and a pointing device, such as a mouse, trackball or touchpad. Other input devices (not shown) may include a joystick, game pad,satellite dish, scanner, or the like.

These and other input devices are often connected to the processor 2610through a user input interface that is coupled to the system bus 2612,but may be connected by other interface and bus structures, such as aparallel port, game port or a universal serial bus (USB). A monitor, adisplay 2662, or other type of display device is also connected to thesystem bus 2612 via an interface, such as a video interface. In additionto the display 2662, the system 2600 may also include other peripheraloutput devices, such as speakers 2666 and printers 2664, which may beconnected through an output peripheral interface.

A source sentence to be translated by the translation application 2630may be for example, entered from the keyboard 2658 and selected on thescreen of the display 2662. As another example, a source sentence to betranslated by the translation application 2630 may be received afterbeing recognized from a graphical input (for example, being recognizedas PDF, TIF, JPG, BMP, and other files) through optical characterrecognition (OCR) applications or after being sent by the fax 2652 andthen scanned by the scanner 2654, etc. A microphone 2656 and a speechrecognition system can also be used and adapted for machine translation.

The system 2600 may operate in a networked environment using logicalconnections to one or more remote computers. The remote computer may bea personal computer, a hand-held device, a server, a router, a networkPC, a peer device or other common network node, and typically includesmany or all of the elements described above relative to the system 2600.The network connections depicted in FIG. 26 can include, for example, alocal area network (LAN) 2690 or a wide area network (WAN), such as theInternet 2680. Such networking environments are commonplace in offices,enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN networking environment, the system 2600 is connectedto the LAN through a network interface 2670 or adapter. When used in aWAN networking environment, the system 2600 may additionally include amodem or other means for establishing communications over the WAN, suchas the Internet. It will be appreciated that the network connectionsshown are exemplary and other means of establishing a communicationslink between the systems and computers may be used.

FIG. 27 illustrates another example of a translation application 2700,such as a machine translation program, in accordance with one embodimentof the invention. The translation application 2700 may include alexical-morphological analyzer 2720 adapted to perform a lexicalanalysis and a lexical-morphological analysis on each element of thesource sentence to generate a lexical-morphological structure of thesource sentence, a syntactic analyzer 2730 adapted to perform asyntactic analysis on the lexical-morphological structure of the sourcesentence, and a semantic analyzer 2740 adapted to perform a semanticanalysis on the source sentence and generate a language-independentsemantic structure for the source sentence.

The translation application 2700 may also include a lexical synthesizer2750 adapted to perform a lexical selection on the language-independentsemantic structure of the source sentence using lexical descriptions andsemantic descriptions in the output language, and a surface structurebuilder 2760 adapted to build a surface structure from thelanguage-independent semantic structure using syntactic descriptions andmorphological descriptions of the output language and construct theoutput sentence in the output language. The translation application 2700further includes a morphological synthesizer 2770 adapted to perform amorphological synthesis on the surface structure using morphologicaldescriptions of the output language and synthesize the output sentence.Additionally, the translation application 2700 may also include a userinterface 2710 for input/output and a database 2780 for storing variouslinguistic descriptions and intermediate information, for exampleratings, pragmatic information, semantic structures of previoussentences, etc.

FIG. 28 is another example of a translation module 2800 according to oneembodiment of the invention. The translation module 2800 may include aanalyzer component 2810 to translate a source sentence in an inputlanguage into a language-independent semantic structure and asynthesizer component 2820 to synthesize an output sentence in an outputlanguage from the language-independent semantic structure of the sourcesentence using various linguistic descriptions. The translation module2800 may also interface with a program interface 2860 and a userinterface 2870 to interact with other programs and a user, respectively.Additionally, the translation module 2800 may include a memory 2850or/and a database 2840 for storing various intermediate information.

The translation module 2800 may interact via the program interface 2860with other applications. For example, the translation module 2800 mayreceive a source sentence from a speech recognition application 2882after converting the source sentence into a text after speechrecognition. As another example, a source sentence may be received froman optical character recognition (OCR) application 2884 after convertingan image of the source sentence into a text after optical recognition ofthe image. The program interface 2860, the user interface 2820, and thenetwork interface 2886, etc., are used to provide communication betweenthe translation module 2800 and its users via a LAN or WAN, such as theInternet.

A typological analysis for the invention was performed for variouslinguistic families, including Indo-European (Slavic, Germanic, andRomanic languages), Finno-Ugrian, Turkic, Oriental, and Semitic.Embodiments of the invention may be applied to many languages,including, but not limited to, English, French, German, Italian,Russian, Spanish, Ukrainian, Dutch, Danish, Swedish, Finnish,Portuguese, Slovak, Polish, Czech, Hungarian, Lithuanian, Latvian,Estonian, Greek, Bulgarian, Turkish, Tatar, Hindi, Serbian, Croatian,Romanian, Slovenian, Macedonian Japanese, Korean, Chinese, Arabic,Hindi, Hebrew, Swahili, among others.

The invention is superior to the known art as it uses various naturallanguage descriptions which can reflect all the complexities of alanguage, rather than simplified or artificial descriptions. As aresult, one or more sentences in a given natural language are generatedfrom a generalized data structure, such as a semantic structure.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A computer system adapted to synthesize a sentence into an outputlanguage, the computer system comprising: a lexical synthesizer adaptedto perform a lexical selection on a language-independent semanticstructure of the sentence using semantic descriptions and lexicaldescriptions of the output language, wherein the lexical synthesizer isadapted to apply semantic structure correction rules to thelanguage-independent semantic structure to overcome asymmetries betweenthe language-independent semantic structure and the syntactic structureof the sentence in the output language, and wherein the semanticdescriptions and the lexical descriptions of the output language arecombined into lexical-semantic descriptions; a surface structure builderadapted to build a surface structure of an output sentence in the outputlanguage from the language-independent semantic structure usingsyntactic descriptions and morphological descriptions of the outputlanguage, wherein the surface structure of the output sentence is builtat least in part based on ratings of syntactic constructions forelements of the source sentence; a morphological synthesizer adapted toperform a morphological synthesis on the surface structure of the outputsentence in the output language using morphological descriptions of theoutput language and to construct the output sentence in the outputlanguage; and a linear order synthesizer adapted to determine a linearorder for and restore movements on the syntactic structure of the outputsentence in the output language.
 2. The computer system of claim 1,wherein the morphological synthesis is performed on lexical meanings ofconstituent cores of the surface structure of the output sentence in theoutput language.
 3. The computer system of claim 1, wherein the one ormore semantic structure correction rules include one or more semantemescalculating rules or one or more normalization rules for transformingthe structure of the output sentence.
 4. The computer system of claim 1,wherein obtaining a language-independent semantic structure to representthe meaning of the sentence includes (1) performing a rough syntacticanalysis on the sentence to generate a graph of generalized constituentsand (2) performing a precise syntactic analysis on the graph of thegeneralized constituents to generate one or more syntactic trees torepresent the sentence from the graph of the generalized constituents.5. The computer system of claim 1, wherein applying semantic structurecorrection rules to the language-independent semantic structure toovercome asymmetries includes the use of semanteme calculating rules andsemanteme normalization rules to remove asymmetries.
 6. The computersystem of claim 1, wherein the syntactic structure of the sentenceincludes deep slots and lexical meanings, wherein thelanguage-independent semantic structure is generated by: (1)substituting each lexical meaning of the sentence in the source languagewith a corresponding language-independent semantic class, (2) confirminga linear order of lexical meanings, and (3) after the linear order isconfirmed, deleting surface slots when generating thelanguage-independent semantic structure, and wherein only deep slots anddeep slot descriptions are used to build the language-independentsemantic structure of the sentence.
 7. A computer system adapted totranslate the meanings of a source sentence from an input language intoan output language, comprising: a source sentence analyzer adapted toanalyze meanings of the source sentence using linguistic descriptions ofthe source language and to construct a language-independent semanticstructure to represent the meanings of the source sentence; and anoutput sentence synthesizer adapted to synthesize an output sentence torepresent the meanings of the source sentence in an output language fromthe language-independent semantic structure using information fromlinguistic descriptions of the output language, wherein the outputsentence synthesizer comprises a linear order synthesizer adapted todetermine a linear order for and restore movements on the syntacticstructure of the output sentence in the output language, wherein theoutput sentence analyzer is adapted to apply semantic structurecorrection rules to the language-independent semantic structure toovercome asymmetries between the language-independent semantic structureand the syntactic structure of the output sentence in the outputlanguage, wherein a surface structure of the output sentence is built atleast in part based on ratings of syntactic constructions for elementsof the source sentence, and wherein applying semantic structurecorrection rules to the language-independent semantic structure toovercome asymmetries includes the use of semanteme calculating rules andsemanteme normalization rules to remove asymmetries.
 8. The computersystem of claim 7, wherein the source analyzer further comprises alexical-morphological analyzer adapted to perform a lexical analysis anda lexical-morphological analysis on each element of the source sentenceand generating a lexical-morphological structure of the source sentence.9. The computer system of claim 7, wherein the source analyzer furthercomprises a rough syntactic analyzer adapted to perform a roughsyntactic analysis on the lexical-morphological structure of the sourcesentence and generate a graph of generalized constituents from thelexical-morphological structure of the source sentence.
 10. The computersystem of claim 9, wherein the source analyzer further comprises aprecise syntactic analyzer adapted to perform a precise syntacticanalysis on the graph of the generalized constituents and generate asyntactic structure of the source sentence from the graph of thegeneralized constituents.
 11. The computer system of claim 7, whereinthe source analyzer further comprises a semantic analyzer adapted toperform a semantic analysis on the syntactic structure of the sourcesentence and generate the language-independent semantic structure forthe source sentence.
 12. The computer system of claim 7, wherein theoutput synthesizer further comprises a lexical synthesizer adapted toperform a lexical selection on the language-independent semanticstructure using the lexical descriptions of the output language and thesemantic descriptions.
 13. The computer system of claim 7, wherein theoutput synthesizer further comprises a surface structure builder adaptedto build a surface structure from the language-independent semanticstructure using syntactic descriptions and the lexical descriptions ofthe output language.
 14. The computer system of claim 7, wherein theoutput synthesizer further comprises a linear order synthesizer adaptedto determine a linear order and restoring movements on the syntacticstructure of the output sentence in the output language.
 15. Thecomputer system of claim 7, wherein the output synthesizer furthercomprises a morphological synthesizer adapted to perform a morphologicalsynthesis on the surface structure using morphological descriptions ofthe output language and construct the output sentence in the outputlanguage.
 16. The computer system of claim 15, wherein the morphologicalsynthesis is performed on lexical meanings of constituent cores of thesurface structure.
 17. The computer system of claim 7, wherein theoutput language comprises a natural language selected from the groupconsisting of English, French, German, Italian, Russian, Spanish,Ukrainian, Dutch, Danish, Swedish, Finnish, Portuguese, Slovak, Polish,Czech, Hungarian, Lithuanian, Latvian, Estonian, Greek, Bulgarian,Turkish, Tatar, Hindi, Serbian, Croatian, Romanian, Slovenian,Macedonian, Japanese, Korean, Chinese, Arabic, Hebrew, and Swahili. 18.A computer system adapted to represent a source sentence from a sourcelanguage into an output sentence in an output language, the computersystem comprising: a lexical-morphological analyzer adapted to perform alexical analysis and a lexical-morphological analysis on each element ofthe source sentence to generate a lexical-morphological structure of thesource sentence; a syntactic analyzer adapted to perform a syntacticanalysis on the lexical-morphological structure of the source sentence;a semantic analyzer adapted to perform a semantic analysis on the sourcesentence and generate a language-independent semantic structure for thesource sentence; a lexical synthesizer adapted to perform a lexicalselection on the language-independent semantic structure of the sourcesentence using lexical descriptions of the output language and semanticdescriptions, wherein the lexical synthesizer is adapted to combined thesemantic descriptions and the lexical descriptions of the outputlanguage into lexical-semantic descriptions; a surface structure builderadapted to build a surface structure of the output sentence in theoutput language from the language-independent semantic structure usingsyntactic descriptions and the lexical descriptions of the outputlanguage and construct the output sentence in the output language,wherein the surface structure of the output sentence in the outputlanguage is built at least in part based on ratings of syntacticconstructions for elements of the source sentence; and a linear ordersynthesizer adapted to determine a linear order for and restoremovements on the syntactic structure of the output sentence in theoutput language.
 19. The computer system of claim 18, furthercomprising: a morphological synthesizer adapted to perform amorphological synthesis on the surface structure of the output sentencein the output language using morphological descriptions of the outputlanguage and synthesize the output sentence.
 20. A computer systemadapted to synthesize a sentence into an output sentence in an outputlanguage, the computer system comprising: a lexical synthesizer adaptedto perform a lexical selection on a language-independent semanticstructure of the sentence using lexical descriptions of the outputlanguage and semantic descriptions, wherein the lexical synthesizer isadapted to apply semantic structure correction rules to thelanguage-independent semantic structure to overcome asymmetries betweenthe language-independent semantic structure and a syntactic structure ofthe output sentence in the output language, wherein the lexicalsynthesizer combines the semantic descriptions and the lexicaldescriptions of the output language into lexical-semantic descriptions;a surface structure builder adapted to build a surface structure of theoutput sentence in the output language from the language-independentsemantic structure using syntactic descriptions and lexical descriptionsof the output language, the syntactic descriptions and the lexicaldescriptions being taken from the combined lexical-semanticdescriptions, wherein the surface structure of the output sentence inthe output language is built at least in part based on ratings ofsyntactic constructions for elements of the source sentence; a linearorder synthesizer adapted to determine a linear order for and restoremovements on the syntactic structure; and a morphological synthesizeradapted to perform a morphological synthesis on the surface structure ofthe output sentence in the output language using morphologicaldescriptions of the output language and construct the sentence in theoutput language.